WO2020186444A1 - Procédé de détection d'objet, dispositif électronique, et support de stockage informatique - Google Patents

Procédé de détection d'objet, dispositif électronique, et support de stockage informatique Download PDF

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Publication number
WO2020186444A1
WO2020186444A1 PCT/CN2019/078629 CN2019078629W WO2020186444A1 WO 2020186444 A1 WO2020186444 A1 WO 2020186444A1 CN 2019078629 W CN2019078629 W CN 2019078629W WO 2020186444 A1 WO2020186444 A1 WO 2020186444A1
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Prior art keywords
pixel
detected
point
feature map
point cloud
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PCT/CN2019/078629
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English (en)
Chinese (zh)
Inventor
张磊杰
陈晓智
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深圳市大疆创新科技有限公司
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Priority to PCT/CN2019/078629 priority Critical patent/WO2020186444A1/fr
Priority to CN201980005385.1A priority patent/CN111316285A/zh
Publication of WO2020186444A1 publication Critical patent/WO2020186444A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks

Definitions

  • the embodiments of the present invention relate to the technical field of object detection, and in particular to an object detection method, electronic equipment, and computer storage medium.
  • Intelligent driving includes automatic driving and assisted driving.
  • One of the core technologies of intelligent driving technology is obstacle detection.
  • the accuracy of obstacle detection results is directly related to the safety and reliability of intelligent driving.
  • a common obstacle detection technology that uses sensors such as cameras, lidars, millimeter wave radars and other sensors on the vehicle to detect dynamic obstacles (such as vehicles, pedestrians) in the road scene, and then obtain the attitude and three-dimensional position of the dynamic obstacles And physical dimensions.
  • the embodiment of the present invention provides an object detection method, electronic equipment and computer storage medium to realize accurate detection of objects.
  • an object detection method includes:
  • the second detection result of the object to be detected is determined.
  • an electronic device includes:
  • Memory used to store computer programs
  • the processor is configured to execute the computer program, specifically:
  • the second detection result of the object to be detected is determined.
  • a vehicle according to an embodiment of the present application includes: a vehicle body and the electronic device as described in the second aspect installed on the vehicle body.
  • a vehicle of an embodiment of the present application includes: a vehicle body and the electronic device as described in the second aspect installed on the vehicle body.
  • an unmanned aerial vehicle includes: a fuselage and the electronic device as described in the second aspect installed on the fuselage.
  • an embodiment of the present application is a computer storage medium, in which a computer program is stored, and the computer program implements the timing control method of an analog communication interface as described in the first aspect when the computer program is executed.
  • the object detection method, electronic device, and computer storage medium provided by the embodiments of the present application obtain the first feature map by performing first processing on the point cloud data of the object to be detected; for each pixel in the first feature map Perform detection to obtain the first detection result of each pixel; and determine the second detection result of the object to be detected according to the first detection result of each pixel. That is, in the embodiment of the present application, by performing pixel-level detection on each pixel in the first feature map, this intensive detection method can increase the recall rate of object detection, thereby improving the accuracy of object detection.
  • FIG. 1 is a schematic diagram of an application scenario involved in an embodiment of this application
  • FIG. 2 is a flowchart of an object detection method provided by an embodiment of the application
  • 3A is a schematic diagram of the framework of a neural network model involved in an embodiment of this application.
  • 3B is a schematic diagram of a neural network model involved in an embodiment of this application.
  • 3C is a schematic diagram of another neural network model involved in an embodiment of this application.
  • FIG. 4 is another flowchart of an object detection method provided by an embodiment of the application.
  • FIG. 5 is another flowchart of the object detection method provided by an embodiment of the application.
  • FIG. 6 is another flowchart of an object detection method provided by an embodiment of the application.
  • FIG. 7 is another flowchart of an object detection method provided by an embodiment of the application.
  • FIG. 8 is a schematic diagram of an electronic device provided by an embodiment of the application.
  • FIG. 9 is another schematic diagram of an electronic device provided by an embodiment of the application.
  • FIG. 10 is a schematic structural diagram of a vehicle provided by an embodiment of the application.
  • FIG. 11 is a schematic structural diagram of a transportation tool provided by an embodiment of the application.
  • FIG. 12 is a schematic structural diagram of a drone provided by an embodiment of the application.
  • words such as “first” and “second” are used to distinguish the same items or similar items with substantially the same function and effect. Those skilled in the art can understand that words such as “first” and “second” do not limit the quantity and order of execution, and words such as “first” and “second” do not limit the difference.
  • the embodiments of this application can be applied to any field that needs to detect objects, such as automatic driving, assisted driving and other intelligent driving fields. It can detect obstacles such as vehicles and pedestrians in road scenes, thereby improving the safety of intelligent driving. Sex.
  • Figure 1 is a schematic diagram of an application scenario involved in an embodiment of this application.
  • the intelligent driving vehicle includes detection equipment.
  • the detection equipment can detect obstacles in the front lane (such as falling rocks, Objects, dead branches, pedestrians, vehicles, etc.) are detected to obtain detection information such as the location, attitude, orientation, and size of obstacles, and based on the detection information to plan the state of intelligent driving, such as lane change, deceleration, or parking.
  • the detection equipment may specifically be radar, ultrasonic detection equipment, Time Of Flight (TOF) ranging detection equipment, visual detection equipment, laser detection equipment, etc., and combinations thereof.
  • TOF Time Of Flight
  • FIG. 1 is a schematic diagram of an application scenario of this application, and the application scenario of the embodiment of this application includes but is not limited to that shown in FIG. 1.
  • FIG. 2 is a flowchart of an object detection method provided by an embodiment of the application. As shown in FIG. 2, the method of the embodiment of the application includes:
  • S101 Perform first processing on the acquired point cloud data of the object to be detected, and obtain a first feature map of the point cloud data.
  • the execution subject of the embodiments of the present application is a device with an object detection function, for example, a detection device, which can be integrated in any electronic device as a part of the electronic device.
  • the detection device may also be a separate electronic device.
  • the electronic device may be a vehicle-mounted device, such as a trip recorder.
  • the carrier of the electronic device may also be an aircraft, an unmanned aerial vehicle, a smart handheld device, a smart handheld device with a stable platform, and the like.
  • Each point cloud in the aforementioned point cloud data includes information such as three-dimensional data and reflectance of the point cloud, wherein the three-dimensional data of the point cloud includes the three-dimensional coordinates of the point cloud in the point cloud data coordinate system.
  • the first processing includes at least one of the following: at least one convolution operation, at least one sampling operation, and at least one stacking operation.
  • the sampling operation may include: a down sampling operation and/or an up sampling operation.
  • the point cloud data of the object to be detected obtained in this step is input into the neural network model shown in FIG. 3A.
  • the neural network model performs the first processing on the point cloud data to obtain the point cloud data.
  • the neural network model includes N-layer feature maps, and at least one convolution and at least one down-sampling operation are included between the first-layer feature maps and the second-layer feature maps.
  • the convolution/down-sampling process is to extract high-level information and Enlarge the receptive field of neurons. At least one convolution and at least one down-sampling operation are included between the second feature map and the third layer feature map.
  • the N-2th feature map to the N-1th layer feature map includes at least one convolution and at least one upsampling operation, where the process of convolution/upsampling is to extract high-level information and perform pixel-level detection.
  • the N-1th feature map to the Nth layer feature map also includes at least one convolution and at least one upsampling operation.
  • FIG. 3A is only an example of a neural network model.
  • the neural network model of the embodiment of the present application includes but is not limited to that shown in FIG. 3A, and the number of operations performed on each feature map shown in FIG. 3A can be calculated according to Set your own resource requirements.
  • the output of the layer 2 feature map can also be used as the input of the N-1 layer feature map.
  • the neural network model used in the embodiment of the present application may also be a segmentation network model as shown in FIG. 3B.
  • the segmentation network model has a structure as shown in the figure, including, for example, 9 layers, where:
  • each layer includes at least one convolution operation (Conv), and the pooling operation (Pooling) is performed between two adjacent layers, and the convolution operation and the pooling operation implement down-sampling.
  • Conv convolution operation
  • Pooling the pooling operation
  • the convolution operation and the pooling operation implement down-sampling.
  • each layer includes at least one convolution operation (Conv), between two adjacent layers is an upward convolution operation (up-Conv), and an upper convolution operation (up-Conv) is implemented Upsampling.
  • Different layers can be stacked.
  • the output of the first layer can be input to the 9th layer for stacking
  • the output of the second layer can be input to the 8th layer for stacking
  • the output of the third layer can be input to the first 7 layers are stacked
  • the output of the 4th layer can be input to the 6th layer for stacking.
  • Stacking can achieve multi-scale feature fusion, feature point-by-point addition, and feature channel dimension splicing, and a pixel-level segmentation map can be obtained, and semantic category judgments can be made for each pixel.
  • the output of the m-th layer is directly stacked with Nm in a stacked manner, so as to realize the fusion of the feature map images of the same layer and the same size, and the image feature map of the m-th layer has more shallow layers due to less convolution.
  • Information After stacking, the output information of the m-th layer can be fused with the information that has not undergone multiple convolutions and pooling before. And then realize the splicing and alignment of shallow information and deep information on the pixel scale. During the stacking process, since the feature map sizes of the same layer are equal, splicing and alignment only need to be aligned in the feature map size, which is very beneficial to the deep semantic information and shallow feature map information in the stacking process of each layer. Fusion.
  • the neural network model used in the embodiment of the present application may be a deep experimental network as shown in FIG. 3C, where the encoder module encodes multi-scale context information by using hole convolution on multiple scales, and The decoder module refines the segmentation result along the object boundary.
  • This deep experimental network can achieve pixel-level classification.
  • each pixel in the first feature map is detected to obtain the detection result of each pixel, and the detection result of each pixel is recorded as the first detection result.
  • the first feature map is input into the neural network model, and the first detection result of each pixel is predicted.
  • the above steps S101 and S102 can be predicted by a neural network model. For example, input the point cloud data of the object to be detected into the neural network model shown in FIG. 3B or FIG. 3C, and first predict the first cloud data A feature map, and then continue to input the first feature map as an input into the neural network model to predict the first detection result of each pixel in the first feature map.
  • the first detection result of the pixel point includes at least one of the following: the semantic category of the pixel point, the orientation of the pixel point, and the distance between the pixel point and the center point of the object to be detected.
  • S103 Determine a second detection result of the object to be detected according to the first detection result of each pixel.
  • the second detection result of the object to be detected includes at least one of the following: the semantic category of the object to be detected, the three-dimensional coordinates of the object to be detected, and the size of the object to be detected.
  • the semantic category of the object to be detected can be obtained; according to the spatial position and orientation of each pixel and the distance between each pixel and the center of the object to be detected, The three-dimensional coordinates and size of the object to be detected.
  • the second detection result of the object to be detected is determined. In this way, object detection based on the pixel level can provide the accuracy of object detection.
  • the object detection method provided by the embodiment of the application obtains a first feature map by performing first processing on the obtained point cloud data of the object to be detected; detects each pixel in the first feature map to obtain each The first detection result of the pixel; and the second detection result of the object to be detected is determined according to the first detection result of each pixel. That is, in the embodiment of the present application, by performing pixel-level detection on each pixel in the first feature map, this intensive detection method can increase the recall rate of object detection, thereby improving the accuracy of object detection.
  • the embodiment of the present application further includes:
  • This step does not limit the way of obtaining the point cloud data of the object to be detected, which is determined according to actual needs.
  • the depth sensor collects point cloud data of the object to be detected, and the electronic device obtains the point cloud data of the object to be detected collected by the depth sensor from the depth sensor.
  • the depth sensor can be installed on the electronic device and is a part of the electronic device.
  • the depth sensor and the electronic device are two components, and the depth sensor is in communication connection with the electronic device, and the depth sensor can transmit the collected point cloud data of the object to be detected to the electronic device.
  • the communication connection between the depth sensor and the electronic device may be a wired connection or a wireless connection, which is not limited.
  • the depth sensor may be radar, ultrasonic detection equipment, TOF ranging detection equipment, laser detection equipment, etc. and combinations thereof.
  • the method for the electronic device to obtain the point cloud data of the object to be detected may also be: the electronic device obtains the first image and the second image collected by the binocular camera of the object to be detected; The first image and the second image obtain point cloud data of the object to be detected.
  • a binocular camera is installed on the vehicle.
  • the binocular camera collects the road map, and then can collect the first image and the second image of the object to be detected.
  • One image is the left-eye image, the second image is the right-eye image, and vice versa.
  • the electronic device matches the pixels of the first image and the second image to obtain the disparity value of each pixel. Based on the triangulation principle, according to the disparity value of each pixel, each pixel in the object to be detected can be obtained. Point cloud data corresponding to each physical point.
  • FIG. 4 is another flowchart of the object detection method provided by the embodiment of the application, and the specific process of performing the first processing on the acquired point cloud data of the object to be detected in the embodiment of the application to obtain the first feature map.
  • the above S101 may include:
  • S201 Perform feature encoding on the acquired point cloud data to obtain an encoding feature map of the point cloud data.
  • the point cloud data obtained in the above steps includes multiple point clouds, and each point cloud is a 4-dimensional vector, including the three-dimensional coordinates and reflectivity of the point cloud.
  • the above point cloud data is disordered. In order to facilitate the accurate and rapid implementation of the subsequent step of obtaining the first feature map, this step can preprocess the obtained disordered point cloud data to minimize the amount of information loss. Next, perform feature encoding of the point cloud data, and then obtain a feature encoding map of the point cloud data.
  • the specific encoding method for feature encoding of the point cloud data can be based on the three-dimensional coordinates and/or reflectivity of each point cloud to perform feature encoding on each point cloud in the point cloud data to obtain the point cloud.
  • the feature code map of the data can be based on the three-dimensional coordinates and/or reflectivity of each point cloud to perform feature encoding on each point cloud in the point cloud data to obtain the point cloud.
  • the object when object detection is performed, the object is usually projected onto the top view, and information such as the position of the object in the top view direction is detected.
  • the top view is a top view
  • the top view includes two-dimensional coordinates of the three-dimensional data projected in the horizontal direction, and height data information and reflectance information corresponding to the two-dimensional coordinates. Therefore, the coding method of performing feature coding on the point cloud data in this step can also be projecting the point cloud data under the top view, and performing feature coding on the point cloud data in the top view direction to obtain a feature coding map of the point cloud data.
  • the size of the first feature map obtained in this step is consistent with the size of the feature encoding map of the point cloud data.
  • the encoded feature map may be input into the neural network model shown in FIG. 3B or FIG. 3C to obtain the first feature map of the point cloud data.
  • the generation speed of the first feature map can be increased, and the first feature can be improved The accuracy of the graph.
  • the foregoing S201 performs feature encoding on the acquired point cloud data to obtain an encoding feature map of the point cloud data, which may include:
  • this step can project the point cloud data to the top view direction, and then compress the point cloud data in the top view direction to obtain the coding characteristics of the point cloud data Figure.
  • the three-dimensional point cloud can be expressed as a four-dimensional vector of (x, y, z, f), and when the feature vector is used, the three-dimensional point cloud set and the points in it are disordered. Point cloud compression can compress disordered point sets with a small loss.
  • FIG. 5 is another flowchart of the object detection method provided by an embodiment of the application. As shown in FIG. 5, the foregoing S300 may include:
  • the point cloud data in the embodiment of the application is three-dimensional point cloud data, and the three-dimensional point cloud data is constrained according to a preset coding space, so as to constrain the three-dimensional point cloud data to the preset coding space.
  • This step can be understood as the compression of point cloud data.
  • the preset coding space can be understood as a cube, and the corresponding coding range is L ⁇ W ⁇ H, where L represents distance, W represents width, and H represents height, and the unit can be m.
  • the embodiment of the present application does not limit the size of the preset coding space, which is specifically determined according to actual needs.
  • this step can constrain the three-dimensional point cloud data in the physical space to the preset coding space L ⁇ W ⁇ H.
  • S302 Perform grid division on the point cloud data in the coding space according to a preset resolution, and determine the characteristics of each grid.
  • the point cloud data is constrained to the coding space according to the above steps, and then the point cloud data in the coding space is grid-divided according to the preset resolution to obtain multiple grids, one of which may include One or more point clouds, or not including point clouds. Then, the characteristics of each grid are determined, for example, based on the point cloud included in the grid.
  • the preset resolution includes resolutions in three directions: length, width, and height.
  • the grid division of the point cloud data in the coding space according to the preset resolution in S302 may include the following step A1:
  • Step A1 Perform grid division on the point cloud data in the coding space according to the resolution in the three directions of length, width and height.
  • the point cloud data is divided in the length direction according to the resolution in the length direction and the length of the preset coding space; according to the resolution in the width direction and the length of the preset coding space Width, dividing the point cloud data in the width direction; dividing the point cloud data in the height direction according to the resolution in the height direction and the width of the preset coding space.
  • the preset coding space is L ⁇ W ⁇ H
  • the preset resolutions in the three directions of length, width, and height are: dl, dw, dh, so that the coding space L ⁇ W is determined according to dl, dw, and dh.
  • the point cloud data in ⁇ H is divided into grids, and the size of each grid obtained is L/dl ⁇ W/dw ⁇ H/dh.
  • determining the characteristics of each grid in the foregoing S302 may specifically include the following step B1:
  • Step B1 Determine the characteristics of the grid according to the number of point clouds included in the grid and/or the reflectivity of the point clouds included in the grid.
  • the size of each grid is obtained as L/dl ⁇ W/dw ⁇ H/dh, and then the number of point clouds included in each grid and/or the number of point clouds included in the grid is obtained
  • the reflectivity determines the characteristics of the grid according to the number of point clouds included and/or the reflectivity of the point clouds included in the grid.
  • the feature of the grid is determined according to the number of point clouds included. For example, if the grid includes a point cloud, the feature of the grid is determined to be 1. If the grid does not include a point cloud, Determine the feature of this grid as 0.
  • the embodiment of the application determines the coding feature map of the point cloud data in the top view.
  • the height information is lost, and the distance information and width information exist in the top view. Therefore, the height information needs to be extracted to obtain the final point cloud data. Encoding feature map.
  • the scale of the coded feature map is L ⁇ W ⁇ H is 80 ⁇ 40 ⁇ 4, and the resolution of length, width and height are all 0.1, and the final coded feature map obtained is C ⁇ A ⁇ B which is 80 ⁇ 800 ⁇ 400.
  • the object detection method provided by the embodiment of the application constrains the point cloud data to a preset coding space; according to the preset resolution, the point cloud data in the coding space is divided into grids, and each grid is determined In the top view direction, according to the characteristics of each grid, the coding feature map of the point cloud data is obtained, and then the coding feature map is accurately obtained.
  • Fig. 6 is another flowchart of the object detection method provided by the embodiment of the application.
  • the embodiment of the present application involves detecting each pixel in the first feature map to obtain each
  • the specific process of the first detection result of the pixel point is described.
  • steps S401 to S403 are the specific process of obtaining the orientation of each pixel
  • step S404 is the specific process of obtaining the semantic category of each pixel
  • steps S405 and S406 are obtaining the center point of each pixel and the object to be detected.
  • the specific process of the distance may include:
  • the first feature map is divided into multiple intervals, specifically, the first feature map is divided into multiple intervals along the circumferential direction with the center of the first feature map as the center.
  • the center of the first feature map as the center of the circle, divide the first feature map into several intervals according to [-180°, 180°], and determine the center of each interval.
  • S402 Predict the interval to which the pixel point belongs, and the position coordinate of the pixel point in the interval to which the pixel point belongs.
  • the interval to which an object orientation belongs will be predicted, and the residual amount of the interval to which the pixel belongs.
  • the relative position in that is, the position coordinate of the pixel in the interval.
  • the prediction of the interval to which each pixel in the first feature map belongs is used as a classification problem, and the prediction of the position coordinate of each pixel in the interval to which it belongs is used as a regression problem.
  • the first feature map can be input into the trained prediction model to predict the interval to which each pixel in the first feature map belongs and the position coordinate of each pixel in the interval to which it belongs.
  • S403 Determine the orientation of the pixel point according to the angle of the interval to which the pixel point belongs and the position coordinate of the pixel point in the interval to which the pixel point belongs.
  • the angle of the interval to which the pixel belongs and the position coordinate of the pixel in the interval are predicted, the specific angle of the pixel can be determined, and the orientation of the pixel can be determined according to the specific angle of the pixel.
  • Test results can include:
  • S404 Perform semantic category detection on the pixel to obtain the semantic category of the pixel.
  • the feature prediction is performed on each pixel in the first feature map in S102 to obtain each pixel.
  • the first detection result of the pixel point may include:
  • S405 Perform position detection on the pixel point to obtain the distance between the pixel point and the center point of the object to be detected.
  • the vector distance between each pixel and the center point of the object to be detected can be obtained.
  • the orientation, semantic category, and distance of each pixel from the center of the object to be detected are obtained respectively, thereby achieving Accurate acquisition of the first detection result of the pixel.
  • FIG. 7 is another flowchart of the object detection method provided by the embodiment of the application.
  • the embodiment of the present application relates to obtaining the pending detection result according to the first detection result of each pixel.
  • the specific process of detecting the second detection result of the object is a specific process of determining the semantic category of the object to be detected
  • step S502 is a specific process of determining the size of the object to be detected.
  • the foregoing S103 may include:
  • each pixel in the first feature map can be clustered to determine the semantic category of the object to be detected. For example, by clustering pixels with the same semantic category, one or more clustering results can be obtained. The clustering result with the largest number of pixels included in the multiple clustering results is regarded as the final clustering result. The semantic category corresponding to the final clustering result is determined as the semantic category of the object to be detected.
  • the foregoing S501 may include, for example, step C1 and step C2;
  • Step C1 cluster the pixels in the first feature map according to the semantic category of each pixel in the first feature map, and obtain the cluster area.
  • the semantic category of each pixel in the first feature map clusters the pixels in the first feature map, and multiple candidate cluster regions may be obtained, and the largest candidate among the multiple candidate cluster regions
  • the cluster area is determined as the cluster area.
  • one candidate cluster area including the most pixel points among the plurality of candidate cluster areas is determined as the cluster area.
  • the foregoing method of clustering the pixels may be a bottom-up gradual clustering method.
  • Step C2 Determine the semantic category of the object to be detected according to the semantic category of each pixel in the cluster area.
  • the semantic category of the object to be detected is determined. For example, if the semantic category of each first pixel in the cluster area is a pedestrian, then the semantic category of the object to be detected is determined to be a pedestrian.
  • S502. Determine the size of the cluster area according to the spatial position of each pixel in the cluster area and the distance between each pixel in the cluster area and the center point of the object to be detected. center.
  • the position information of the pixel can be determined according to the preset resolution and the grid corresponding to the pixel.
  • the above-mentioned first feature map is obtained based on the encoding feature map, and the encoding feature map is obtained by rasterizing at a certain resolution.
  • Each pixel can be understood as a grid, so that it can be based on the preset encoding space and preset Resolution, the position information of the grid can be obtained, the position information of the pixel corresponding to the grid can be determined according to the position information of the grid, and the accuracy of the position information is the resolution value.
  • the center position of the cluster area can be determined based on the spatial position of each pixel in the cluster area and the distance between each pixel in the cluster area and the center point of the object to be detected.
  • the center of the clustering area coincides with the center point of the object to be detected, so that the center of the object to be detected can be determined according to the spatial position of each pixel and the distance between each pixel and the center of the object to be detected The position of the point, and then determine the center position of the clustering area.
  • the foregoing S502 includes step D;
  • Step D According to the spatial position of each pixel in the cluster area, the distance between each pixel in the cluster area and the center point of the object to be detected, and the cluster area The first weight of each of the pixels within determines the center of the cluster area.
  • the first weight is used as the weight of the distance between the pixel point and the center point of the object to be detected. In this way, according to the spatial position of each pixel in the cluster area, each pixel point in the cluster area and the center point of the object to be detected In the process of determining the center of the clustering area, the first weight of the distance between each pixel and the center point of the object to be detected is increased in the process of determining the center of the clustering area, thereby improving the accuracy of calculation of the center of the clustering area.
  • the first weight of each pixel in the cluster area is the semantic category probability value of each pixel in the cluster area.
  • S503 Determine the orientation of the cluster area according to the orientation of each pixel in the cluster area.
  • each pixel has an orientation, so that the orientation of the cluster area can be determined according to the orientation of each pixel.
  • the orientation of the most pixels in the cluster area is taken as the orientation of the cluster area.
  • the orientation of the class area is also a.
  • the foregoing S503 includes step E;
  • Step E Determine the orientation of the cluster area according to the orientation of each pixel in the cluster area and the second weight of each pixel in the cluster area.
  • the second weight of the orientation of each pixel is added, thereby improving the accuracy of determining the orientation of the clustering area.
  • the second weight of each pixel in the cluster area may be a semantic category probability value of each pixel in the cluster area.
  • the orientation of the object to be detected can be determined according to the orientation of the cluster area, for example, the orientation of the cluster area is determined as the orientation of the object to be detected.
  • S504 Determine the size of the object to be detected according to the center of the cluster area and the orientation of the cluster area.
  • the size of the cluster area can be determined, and then the size of the object to be detected can be determined according to the size of the cluster area.
  • the foregoing S404 may include step F1 and step F2.
  • Step F1 Determine the largest circumscribed rectangular frame of the cluster area according to the center of the cluster area and the orientation of the cluster area.
  • the largest circumscribed rectangular frame of the cluster area is obtained by fitting.
  • Step F2 Determine the size of the object to be detected according to the largest circumscribed rectangular frame of the cluster area.
  • the size of the largest circumscribed rectangular frame of the cluster area is taken as the size of the object to be detected.
  • this embodiment of the present application may also determine the three-dimensional coordinates of the object to be detected according to the largest circumscribed rectangular frame of the clustering area. That is, the three-dimensional coordinates of the largest circumscribed rectangular frame are used as the three-dimensional coordinates of the object to be detected.
  • the object inspection method provided by the embodiment of the present application clusters the pixels in the first feature map according to the semantic category of each pixel in the first feature map to determine the semantic category of the object to be detected; according to the clustering
  • the orientation of each pixel in the area determine the orientation of the cluster area; determine the largest circumscribed rectangular frame of the object to be detected according to the center of the cluster area and the orientation of the cluster area, according to The maximum circumscribed rectangular frame can determine the size and three-dimensional coordinates of the object to be detected, thereby achieving accurate determination of the semantic category of the object to be detected, the three-dimensional coordinates of the object to be detected, and the size of the object to be detected.
  • FIG. 8 is a schematic diagram of an electronic device provided by an embodiment of the application.
  • the electronic device 200 of the embodiment of the application includes at least one memory 210 and at least one processor 220.
  • the memory 210 is used to store a computer program;
  • the processor 220 is used to execute the computer program, specifically:
  • the processor 220 when executing a computer program, is specifically configured to perform first processing on the acquired point cloud data of the object to be detected to obtain a first feature map; to detect each pixel in the first feature map to obtain The first detection result of each pixel; and the second detection result of the object to be detected is determined according to the first detection result of each pixel.
  • the electronic device of the embodiment of the present application may be used to execute the technical solution of the method embodiment shown above, and its implementation principles and technical effects are similar, and will not be repeated here.
  • the processor 220 is specifically configured to perform feature encoding on the acquired point cloud data to obtain an encoding feature map of the point cloud data; perform the encoding feature map on the encoding feature map.
  • the first process is to obtain the first characteristic map.
  • the processor 220 is specifically configured to compress the point cloud data in a top view direction to obtain an encoding feature map of the point cloud data.
  • the processor 220 is specifically configured to constrain the point cloud data to a preset coding space; according to a preset resolution, perform a calculation of the point cloud data in the coding space Perform grid division and determine the characteristics of each grid; and in the top view direction, obtain the coded feature map of the point cloud data according to the characteristics of each grid.
  • the preset resolution includes: resolution in three directions of length, width, and height; the processor 220 is specifically configured to perform according to the three directions of length, width, and height.
  • the point cloud data in the coding space is divided into grids.
  • the processor 220 is specifically configured to divide the point cloud data in the length direction according to the resolution in the length direction and the length of the preset encoding space; The resolution in the width direction and the width of the preset encoding space are divided into the point cloud data in the width direction; according to the resolution in the height direction and the width of the preset encoding space, The point cloud data is divided in the height direction.
  • the processor 220 is specifically configured to determine the number of point clouds included in the grid and/or the reflectivity of the point clouds included in the grid. The characteristics of the grid.
  • the scale of the encoding feature map is C ⁇ A ⁇ B, where C is determined by the ratio of the height of the preset encoding space to the resolution in the height direction, and A is determined by the ratio of the length of the preset coding space to the resolution in the length direction, and the B is determined by the ratio of the width of the preset coding space to the resolution in the width direction.
  • the first processing includes at least one of the following: at least one convolution operation, at least one sampling operation, and at least one stacking operation.
  • the sampling operation includes: a down sampling operation and/or an up sampling operation.
  • the size of the first feature map and the encoding feature map are the same.
  • the first detection result of the pixel point includes at least one of the following: the semantic category of the pixel point, the orientation of the pixel point, the center of the pixel point and the object to be detected The distance of the point.
  • the first detection result of the pixel includes the semantic category of the pixel
  • the processor 220 is specifically configured to perform category detection on the pixel to obtain the pixel The semantic category.
  • the first detection result of the pixel point includes the orientation of the pixel point
  • the processor 220 is specifically configured to use the center of the first feature map as the center of the circle and move along the circumference.
  • Direction divide the first feature map into a plurality of intervals; predict the interval to which the pixel point belongs, and the position coordinate of the pixel point in the interval; and according to the angle of the interval to which the pixel point belongs, and The position coordinates of the pixel point in the interval to which it belongs determine the orientation of the pixel point.
  • the first detection result of the pixel point includes the distance between the pixel point and the center point of the object to be detected
  • the processor 220 is specifically configured to perform Position detection to obtain the distance between the pixel point and the center point of the object to be detected.
  • the second detection result of the object to be detected includes at least one of the following: semantic category of the object to be detected, size of the object to be detected, and three-dimensional coordinates of the object to be detected .
  • the second detection result of the object to be detected includes the semantic category of the object to be detected
  • the processor 220 is specifically configured to perform according to each of the The semantic category of pixels is to cluster the pixels in the first feature map to determine the semantic category of the object to be detected.
  • the processor 220 is specifically configured to perform according to the semantic category of each pixel in the first feature map.
  • the semantic category of the pixel points, cluster the pixels in the first feature map to obtain the cluster area; according to the semantic category of each pixel in the cluster area, determine the object to be detected Semantic category.
  • the method of clustering the pixels is a method of gradually clustering from bottom to top.
  • the second detection result of the object to be detected includes the size of the object to be detected
  • the processor 220 is specifically configured to determine the size of each pixel in the cluster area. Determine the center of the cluster area according to the spatial position of each pixel in the cluster area and the center point of the object to be detected; determine the center of the cluster area according to each pixel in the cluster area Determine the orientation of the cluster area; determine the size of the object to be detected according to the center of the cluster area and the orientation of the cluster area.
  • the processor 220 is specifically configured to determine the relationship between each pixel in the cluster area and the spatial position of each pixel in the cluster area. The distance of the center point of the object to be detected and the first weight of each pixel in the cluster area determine the center of the cluster area.
  • the processor 220 is specifically configured to perform according to the orientation of each pixel in the cluster area, and the second position of each pixel in the cluster area.
  • the weight determines the orientation of the cluster area.
  • the first weight and/or the second weight of each pixel in the cluster area is a semantic category probability value of each pixel in the cluster area.
  • the processor 220 is specifically configured to determine the largest circumscribed rectangular frame of the cluster area according to the center of the cluster area and the orientation of the cluster area; The largest circumscribed rectangular frame of the clustering area determines the size of the object to be detected.
  • the processor 220 is specifically configured to use the center of the cluster region as the center of the largest circumscribed rectangular frame, and use the orientation of the cluster region as a constraint to obtain The largest circumscribed rectangular frame of the cluster area.
  • the processor 220 is further configured to determine the three-dimensional coordinates of the object to be detected according to the largest circumscribed rectangular frame of the cluster area.
  • the processor 220 is further configured to obtain point cloud data of the object to be detected.
  • the processor 220 is specifically configured to obtain the point cloud data of the object to be detected collected by the depth sensor.
  • the processor 220 is specifically configured to obtain the first image and the second image collected by the binocular camera on the object to be detected; according to the first image and the second image Image to obtain point cloud data of the object to be detected.
  • the electronic device 200 of the embodiment of the present application may be used to implement the technical solutions of the method embodiments shown above, and its implementation principles and technical effects are similar, and will not be repeated here.
  • FIG. 9 is another schematic diagram of an electronic device provided by an embodiment of the application. Based on the foregoing embodiment, as shown in FIG. 9, the electronic device 200 of the embodiment of the application further includes a binocular camera 230,
  • the binocular camera 230 is used to collect the first image and the second image of the object to be detected;
  • the processor 220 is specifically configured to obtain the first image and the second image collected by the binocular camera; and obtain a point cloud of the object to be detected according to the first image and the second image data.
  • each point cloud in the point cloud data includes three-dimensional data and reflectivity of the point cloud.
  • the electronic device of the embodiment of the present application can be used to implement the technical solution of the method embodiment shown above, and its implementation principles and technical effects are similar, and will not be repeated here.
  • FIG. 10 is a schematic structural diagram of a vehicle provided by an embodiment of the application.
  • a vehicle 50 in this embodiment includes a body 51 and an electronic device 52 installed on the body 51.
  • the electronic device 52 is the electronic device shown in FIG. 8 or FIG. 9, and the electronic device 52 is used for object detection, for example, detecting obstacles on the running path of the vehicle.
  • the electronic device 52 is installed on the roof of the vehicle body 51. If the electronic device is the electronic device shown in FIG. 9, the binocular camera in the electronic device 52 can face the front or the rear of the vehicle for collecting One image and second image.
  • the electronic device 52 is installed on the front windshield of the vehicle body 51, or the electronic device 52 is installed on the rear windshield of the vehicle body 51.
  • the electronic device 52 is installed on the front of the vehicle body 51, or the electronic device 52 is installed on the rear of the vehicle body 51.
  • the embodiment of the present application does not limit the installation position of the electronic device 52 on the body 51, which is specifically determined according to actual needs.
  • the vehicle of the embodiment of the present application may be used to implement the technical solution of the embodiment of the object detection method shown above, and its implementation principles and technical effects are similar, and will not be repeated here.
  • FIG. 11 is a schematic structural diagram of a vehicle provided by an embodiment of this application.
  • the vehicle 60 of this embodiment includes: a vehicle body 61 and an electronic device 62 installed on the vehicle body 61.
  • the electronic device 62 is the electronic device shown in FIG. 8 or FIG. 9, and the electronic device 62 is used for object detection, such as detecting obstacles on the running path of a vehicle.
  • the vehicle 60 in this embodiment may be a ship, automobile, bus, railway vehicle, aircraft, railway locomotive, scooter, bicycle, etc.
  • the electronic device 62 can be installed on the front, rear, or middle of the vehicle body 61, etc.
  • the embodiment of the present application does not limit the installation position of the electronic device 62 on the vehicle body 61, and is specifically determined according to actual needs.
  • the transportation tool of the embodiment of the present application can be used to implement the technical solution of the above-mentioned object detection method embodiment, and its implementation principles and technical effects are similar, and will not be repeated here.
  • this intensive detection method can make the object detection recall high.
  • the orientation and 3D position are predicted at the pixel level, and then weighted and fused. This method can obtain more reliable and stable positioning accuracy.
  • the orientation prediction and the priori that the target frame in the top view is unlikely to overlap, effectively reduce the calculation amount of rectangular frame fitting.
  • the solution provided by the patent can efficiently realize high-precision three-dimensional dynamic obstacle positioning, which is very suitable for dynamic obstacle perception in autonomous driving scenarios.
  • FIG. 12 is a schematic structural diagram of a drone provided by an embodiment of the application.
  • the drone 100 shown in FIG. 12 can be a multi-rotor, fixed-wing and other types of drones, where the multi-rotor drone can include Quad-rotor, hexa-rotor, octo-rotor and other drones including other numbers of rotors.
  • a rotary wing drone is taken as an example for description.
  • the drone 100 may include a power system 150, a flight control system 160, a frame, and electronic equipment 120 fixed on the frame.
  • the frame may include a fuselage and a tripod (also called a landing gear).
  • the fuselage may include a center frame and one or more arms connected to the center frame, and the one or more arms extend radially from the center frame.
  • the tripod is connected with the fuselage and used for supporting the UAV 100 when it is landed.
  • the power system 150 may include one or more electronic speed regulators (referred to as ESCs) 151, one or more propellers 153, and one or more motors 152 corresponding to the one or more propellers 153, wherein the motors 152 are connected to Between the electronic governor 151 and the propeller 153, the motor 152 and the propeller 153 are arranged on the arm of the UAV 110; the electronic governor 151 is used to receive the driving signal generated by the flight control system 160 and provide driving according to the driving signal Current is supplied to the motor 152 to control the speed of the motor 152.
  • the motor 152 is used to drive the propeller to rotate, thereby providing power for the flight of the drone 100, and the power enables the drone 100 to realize one or more degrees of freedom of movement.
  • the drone 100 may rotate around one or more rotation axes.
  • the aforementioned rotation axis may include a roll axis (Roll), a yaw axis (Yaw), and a pitch axis (pitch).
  • the motor 152 may be a DC motor or an AC motor.
  • the motor 152 may be a brushless motor or a brushed motor.
  • the flight control system 160 may include a flight controller 161 and a sensing system 162.
  • the sensing system 162 is used to measure the attitude information of the drone, that is, the position information and state information of the drone 110 in space, such as three-dimensional position, three-dimensional angle, three-dimensional velocity, three-dimensional acceleration, and three-dimensional angular velocity.
  • the sensing system 162 may include, for example, at least one of sensors such as a gyroscope, an ultrasonic sensor, an electronic compass, an inertial measurement unit (IMU), a vision sensor, a global navigation satellite system, and a barometer.
  • the global navigation satellite system may be a global positioning system (Global Positioning System, GPS).
  • the flight controller 161 is used to control the flight of the drone 100, for example, it can control the flight of the drone 110 according to the attitude information measured by the sensor system 162. It should be understood that the flight controller 161 can control the drone 100 according to pre-programmed program instructions, and can also control the drone 100 by responding to one or more control instructions from the control terminal 140.
  • the electronic device 120 is used to implement object detection and send the detection result to the flight control system 160.
  • the above flight control system 160 controls the flight of the drone 100 according to the object detection result.
  • the electronic device further includes a photographing component, and the photographing component is a binocular camera for collecting the first image and the second image.
  • the photographing component of this embodiment at least includes a photosensitive element, and the photosensitive element is, for example, a Complementary Metal Oxide Semiconductor (CMOS) sensor or a Charge-coupled Device (CCD) sensor.
  • CMOS Complementary Metal Oxide Semiconductor
  • CCD Charge-coupled Device
  • the unmanned aerial vehicle of the embodiment of the present application can be used to implement the technical solution of the object detection method in the foregoing method embodiment, and its implementation principles and technical effects are similar, and will not be repeated here.
  • the embodiment of the present application also provides a computer storage medium.
  • the computer storage medium is used to store the computer software instructions for detecting the above object.
  • the computer can execute various possible object detection methods in the foregoing method embodiments.
  • the processes or functions described in the embodiments of the present application can be generated in whole or in part.
  • the computer instructions can be stored in a computer storage medium, or transmitted from one computer storage medium to another computer storage medium, and the transmission can be transmitted to another by wireless (such as cellular communication, infrared, short-range wireless, microwave, etc.) Website site, computer, server or data center for transmission.
  • the computer storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center integrated with one or more available media.
  • the usable medium may be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, an SSD).
  • a person of ordinary skill in the art can understand that all or part of the steps in the above method embodiments can be implemented by a program instructing relevant hardware.
  • the foregoing program can be stored in a computer readable storage medium. When the program is executed, it is executed. Including the steps of the foregoing method embodiment; and the foregoing storage medium includes: read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disks or optical disks, etc., which can store program codes Medium.

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Abstract

L'invention concerne un procédé de détection d'objet, un dispositif électronique (200) et un support de stockage informatique, le procédé comprenant les étapes consistant à : effectuer un premier traitement sur des données de nuage de points acquises d'un objet à détecter pour acquérir une première carte de caractéristiques (S101) ; effectuer une détection sur chaque pixel dans la première carte de caractéristiques pour acquérir un premier résultat de détection pour chaque pixel (S102) ; et sur la base du premier résultat de détection pour chaque pixel, déterminer un second résultat de détection pour l'objet à détecter (S103). Au moyen de la réalisation d'une détection de niveau de pixel sur chaque pixel dans une carte de caractéristiques, un tel mode de détection dense est capable d'améliorer le taux de rappel de détection d'objet, ce qui améliore ainsi la précision de détection d'objet.
PCT/CN2019/078629 2019-03-19 2019-03-19 Procédé de détection d'objet, dispositif électronique, et support de stockage informatique WO2020186444A1 (fr)

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CN201980005385.1A CN111316285A (zh) 2019-03-19 2019-03-19 物体检测方法、电子设备与计算机存储介质

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