CN112633157A - AGV working area safety real-time detection method and system - Google Patents
AGV working area safety real-time detection method and system Download PDFInfo
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Abstract
The invention discloses a real-time detection method for the safety of an AGV working area, which comprises the following steps: manufacturing an AGV working area map according to the AGV working area; respectively marking the positions of an AGV working area and a goods placement area in an AGV working area graph according to the area coordinates; adding an AGV working area image of the marked position to a video stream of the AGV working area for displaying; processing a video stream of an AGV working area through a neural network model, and acquiring area pixel coordinates of the AGV, goods and people in the video; and judging whether a person enters an AGV working area or not and whether the goods placement position is wrong or not according to the area pixel coordinates and the AGV working area image of the marked position. The method and the device consider the influence of the stored objects in the area on the safety of the area, have better visual effect and improve the safety of the AGV working area.
Description
Technical Field
The invention relates to the technical field of video monitoring, in particular to a method and a system for detecting the safety of an AGV working area in real time.
Background
With the continuous development of intelligent monitoring technology and the increasing maturity of image processing technology, the method of manually monitoring the area is far from meeting the actual requirement. At present, an intelligent monitoring system mainly based on technologies such as artificial intelligence, video analysis and the like makes up for the problem of insufficient manual methods to a certain extent. The intelligent monitoring system has the advantages that monitoring personnel can quickly make decisions according to the actual conditions of the site obtained by videos returned by the monitoring system without going on patrol on site, so that the intelligent monitoring system has wide development space and huge potential market. The existing region detection method mainly adopts technologies of warning region intrusion detection, region people flow statistics and region crowd density detection, wherein the warning region intrusion detection refers to the identification of a target intruding into a warning region, the region people flow statistics refers to the statistics of the people flow entering and leaving a certain region in a certain period of time, and the region crowd density detection refers to the statistics of the target in the certain region. In general, the current area detection technology is to identify an area to be pre-warned by combining a visual analysis algorithm with a camera, and generate a pre-warning function when a person is detected in the area. Aiming at an AGV working area, the influence of objects in the area on the safety of the area is not considered in the current area detection technology, the function is single, and the visual effect is poor.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method and a system for detecting the safety of an AGV working area in real time, so as to solve the problem that the influence of objects in the area on the safety of the area is not considered in the prior art.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a real-time detection method for the safety of an AGV working area comprises the following steps:
manufacturing an AGV working area map according to the AGV working area;
respectively marking the positions of an AGV working area and a goods placement area in an AGV working area graph according to the area coordinates;
adding an AGV working area image of the marked position to a video stream of the AGV working area for displaying;
processing a video stream of an AGV working area through a neural network model, and acquiring area pixel coordinates of the AGV, goods and people in the video;
and judging whether a person enters an AGV working area or not and whether the goods placement position is wrong or not according to the area pixel coordinates and the AGV working area image of the marked position.
Further, the neural network model is constructed based on a deep learning framework tensorflow.
Further, the neural network model is obtained as follows:
acquiring a video stream for monitoring an AGV working area;
capturing pictures from a video stream, and labeling an AGV, goods and people in the pictures to obtain a training set;
training the neural network model through a training set, and acquiring the neural network model capable of identifying the AGV, the goods and the people in the AGV working area.
Further, the video stream is acquired by a monitoring camera; the monitoring camera is selected according to the area and the length-width ratio of the AGV working area; the monitoring camera is installed directly over AGV work area.
Further, the video stream is obtained from the camera head in real time and uninterruptedly through a communication protocol.
Further, the method for judging whether people enter the AGV working area or not and whether the goods placing position is wrong or not comprises the following steps:
if the area determined by the pixel coordinates of the area of the person is intersected with the AGV working area in the AGV working area image, judging that the person enters the AGV working area, otherwise, judging that the person does not enter the AGV working area; if the goods placing area in the AGV working area graph can not completely contain goods, the goods placing position error can be judged.
A real-time AGV work area security detection system, the system comprising:
manufacturing a module: the AGV working area graph is used for manufacturing an AGV working area graph according to the AGV working area;
a marking module: the system is used for marking the positions of an AGV working area and a goods placement area in the AGV working area according to the area coordinates;
an additional module: the AGV working area map is used for attaching the AGV working area map with the determined position to a video stream of the AGV working area for displaying;
an acquisition module: the system comprises a neural network model, a video acquisition module, a video processing module and a display module, wherein the neural network model is used for processing a video stream of an AGV working area and acquiring area pixel coordinates of the AGV, goods and people in the video;
a judging module: and the AGV working area graph is used for judging whether a person enters the AGV working area or not and whether the goods placing position is wrong or not according to the area pixel coordinates and the marked position.
A real-time detection system for AGV work area security, the system comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate according to the instructions to perform the steps of the method described above.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method described above.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the video stream of the AGV working area is processed in real time through the neural network model, the area pixel coordinates of the AGV, the goods and the people in the video are obtained, the area pixel coordinates of the AGV, the goods and the people are compared with the AGV working area image, whether the people enter the AGV working area or not and whether the goods are placed at wrong positions or not are judged, the state of the object in the current area can be obtained and analyzed in real time, the influence of the object stored in the area on the safety of the area is considered, the visual effect is good, and the safety of the AGV working area is improved.
Drawings
FIG. 1 is a diagram of a simple AGV work area;
FIG. 2 is a flow chart of a proposed method of the present invention;
FIG. 3 is a schematic diagram of a person entering an AGV work area;
fig. 4 is a schematic diagram of a cargo placement error.
Detailed Description
The invention is further described with reference to the accompanying drawings, and the following examples are only for better clarity of the technical solutions of the invention, and should not be construed as limiting the scope of the invention.
A real-time detection method for the safety of an AGV working area comprises the following steps:
manufacturing an AGV working area map according to the AGV working area; respectively marking the positions of an AGV working area and a goods placement area in an AGV working area graph according to the area coordinates; adding an AGV working area image of the marked position to a video stream of the AGV working area for displaying; processing a video stream of an AGV working area through a neural network model, and acquiring area pixel coordinates of the AGV, goods and people in the video; and judging whether a person enters an AGV working area or not and whether the goods placement position is wrong or not according to the area pixel coordinates and the AGV working area image of the marked position.
As shown in fig. 2, a real-time detection method for AGV working area security specifically includes the following steps:
step S1: the method comprises the steps of selecting a camera meeting monitoring requirements according to the area and the length-width ratio of an AGV working area to be monitored, then installing the camera right above the AGV working area, and obtaining video streams of the AGV working area in the camera in real time and uninterruptedly within 24 hours by utilizing a communication protocol to obtain video images of regional scenes.
Step S2: and intercepting a certain number of pictures of the AGV working area from the video stream of the AGV working area, and marking the AGV, the goods and the people in the pictures by using a marking tool to obtain a training set.
Step S3: s3.1, constructing a deep neural network model by using a deep learning frame tensorflow in deep learning, and S3.2, training the neural network model by using the training set in the step 2 to obtain the neural network model capable of identifying the AGV, goods and people in the AGV working area.
Step S4: s4.1, a simple AGV working area map is manufactured according to the AGV working area monitored by the camera, as shown in the figure 1, the detailed area coordinates (Xmin, Ymin, Xmax and Ymax) of the AGV working area and the goods placement area are obtained by using a pixel coordinate system of the picture, wherein (Xmin, Ymin, Xmax and Ymax) determine a rectangular area; and S4.2, the graph is attached to the video stream in the camera for display, so that the visualization effect is improved.
Step S5: processing the video stream in the camera in real time by using the neural network model trained in the step 3, identifying the AGV, the goods and the people in the video, and respectively obtaining the region pixel coordinates (Xmin, Ymin, Xmax and Ymax) of the AGV, the goods and the people, so that the states of the AGV, the goods and the people, such as the position of the AGV, whether the placing position of each goods is idle, the placing state of the goods and the like, can be continuously obtained in real time, and then alarming according to a simple AGV working region diagram, and alarming the situations that the placing position of the goods is wrong or the goods are not completely placed into the placing position, and the safety of the working region is influenced when the personnel enter the AGV working region; if the area determined by the area pixel coordinates (PersonXmin, PersonYmin, PersonXmax, PersonYmax) of the person and the area coordinates (WorkXmin, WorkYmin, WorkXmax, WorkYmax) of the AGV working area satisfy (PersonXmin > = WorkXmax or PersonYmin > = WorkYmax or PersonXmax < = WorkXmin or PersonYmax < = WorkYmin), the fact that the person does not enter the AGV working area can be judged, otherwise, the intersection exists between the AGV working area and the area where the person is located, the fact that the person enters the AGV working area is judged, and area alarm is carried out. As shown in fig. 3. According to the judgment of the area coordinates, if the goods are to be placed correctly, the area cannot completely contain the goods, for example: if the cargo placement area is 3 (Load 3Xmin, Load3Ymin, Load3Xmax, Load3 Ymax) and the cargo area coordinates (CargoXmin, CargoYmin, CargoXmax, CargoYmax) cannot be simultaneously met (Load 3Xmin < = CargoXmin, Load3Ymin < = CargoXmin, Load3Xmax > = CargoXmax, and Load3Ymax > = CargoYmax), it can be determined that the cargo placement position is wrong, and an alarm response is made. As shown in fig. 4.
A real-time AGV work area security detection system, the system comprising:
manufacturing a module: the AGV working area graph is used for manufacturing an AGV working area graph according to the AGV working area;
a marking module: the system is used for marking the positions of an AGV working area and a goods placement area in the AGV working area according to the area coordinates;
an additional module: the AGV working area map is used for attaching the AGV working area map with the determined position to a video stream of the AGV working area for displaying;
an acquisition module: the system comprises a neural network model, a video acquisition module, a video processing module and a display module, wherein the neural network model is used for processing a video stream of an AGV working area and acquiring area pixel coordinates of the AGV, goods and people in the video;
a judging module: and the AGV working area graph is used for judging whether a person enters the AGV working area or not and whether the goods placing position is wrong or not according to the area pixel coordinates and the marked position.
A real-time detection system for AGV work area security, the system comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate according to the instructions to perform the steps of the method described above.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method described above.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (9)
1. A real-time detection method for the safety of an AGV working area is characterized by comprising the following steps:
manufacturing an AGV working area map according to the AGV working area;
respectively marking the positions of an AGV working area and a goods placement area in an AGV working area graph according to the area coordinates;
adding an AGV working area image of the marked position to a video stream of the AGV working area for displaying;
processing a video stream of an AGV working area through a neural network model, and acquiring area pixel coordinates of the AGV, goods and people in the video;
and judging whether a person enters an AGV working area or not and whether the goods placement position is wrong or not according to the area pixel coordinates and the AGV working area image of the marked position.
2. The method of claim 1, wherein said neural network model is constructed based on a deep learning framework tenserflow.
3. The method of claim 1, wherein the neural network model is obtained as follows:
acquiring a video stream for monitoring an AGV working area;
capturing pictures from a video stream, and labeling an AGV, goods and people in the pictures to obtain a training set;
training the neural network model through a training set, and acquiring the neural network model capable of identifying the AGV, the goods and the people in the AGV working area.
4. The method of claim 3, wherein the video stream is captured by a surveillance camera; the monitoring camera is selected according to the area and the length-width ratio of the AGV working area; the monitoring camera is installed directly over AGV work area.
5. The method of claim 3, wherein said video stream is captured from said camera via a communication protocol without interruption in real time.
6. The method of claim 1, wherein the step of determining whether the AGV working area is occupied by a human and whether the cargo placement position is incorrect comprises:
if the area determined by the pixel coordinates of the area of the person is intersected with the AGV working area in the AGV working area image, judging that the person enters the AGV working area, otherwise, judging that the person does not enter the AGV working area; if the goods placing area in the AGV working area graph can not completely contain goods, the goods placing position error can be judged.
7. A real-time AGV work area security detection system, comprising:
manufacturing a module: the AGV working area graph is used for manufacturing an AGV working area graph according to the AGV working area;
a marking module: the system is used for marking the positions of an AGV working area and a goods placement area in the AGV working area according to the area coordinates;
an additional module: the AGV working area map is used for attaching the AGV working area map with the determined position to a video stream of the AGV working area for displaying;
an acquisition module: the system comprises a neural network model, a video acquisition module, a video processing module and a display module, wherein the neural network model is used for processing a video stream of an AGV working area and acquiring area pixel coordinates of the AGV, goods and people in the video;
a judging module: and the AGV working area graph is used for judging whether a person enters the AGV working area or not and whether the goods placing position is wrong or not according to the area pixel coordinates and the marked position.
8. A real-time detection system for AGV work area security, characterized in that the system comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 6.
9. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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| CN112633157B (en) | 2024-05-24 |
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