CN110472508A - Lane line distance measuring method based on deep learning and binocular vision - Google Patents
Lane line distance measuring method based on deep learning and binocular vision Download PDFInfo
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- CN110472508A CN110472508A CN201910636651.2A CN201910636651A CN110472508A CN 110472508 A CN110472508 A CN 110472508A CN 201910636651 A CN201910636651 A CN 201910636651A CN 110472508 A CN110472508 A CN 110472508A
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Abstract
The present invention relates to a kind of lane line distance measuring method based on deep learning and binocular vision: including image capture module, the lane detection module based on convolutional neural networks, based on the range finder module of binocular vision.Lane detection module based on convolutional neural networks is for accurately identifying the lane line positioned at vehicle two sides, use vgg16 network frame and full convolutional network FCN, three layers after vgg16 network full articulamentum are become into warp lamination, warp lamination up-samples the characteristic pattern of convolutional layer, to generate a prediction to each pixel, lane line forecasting problem is transformed into image pixel-class to solve, obtains the binary map and instance graph of prediction lane line by convolutional neural networks;Range finder module based on binocular vision, the lane line position that will have been obtained using Binocular Stereo Matching Algorithm SGBM algorithm are extracted the two sides lane line nearest point of camera of adjusting the distance and carry out depth calculation and realize real time distance.
Description
Technical field
The invention belongs to computer vision fields, and in particular to a kind of based on the lane detection of deep learning and ranging side
Method.
Background technique
Deep learning is a branch of machine learning, and the mind of analytic learning is carried out its object is to establish, simulate human brain
Through network, by simulating the mechanism unscrambling data of human brain, essence be by by the combination of extracted low-level feature information from
And high-rise attributive character is formed, to find that the distributed nature of data indicates, deep learning is widely used in text, sound at present
In terms of the identification field of image, especially image recognition, the method for being based primarily upon physical features is identified compared to traditional images,
Have many advantages, such as that accuracy rate height, strong robustness can be realized Weigh sensor image.
Current many vehicles all have the function of the driving of auxiliary driver, for example lane keeps function, lane line early warning function
Energy.The function vehicle can be made to be maintained at lane between appropriate location, this function is for having potential deviation or driving automatically
Trajectory planning and decision in sailing are crucial.Traditional lane detection method is mentioned dependent on height definitionization, manual feature
It takes and heuristic, is typically required post-processing technology, and this often makes computationally intensive, and it is changeable to be unfavorable for road scene
Under application extension.And the ranging of lane line need to rely on lane detection as a result, carrying out ranging using laser radar
Cost is larger, and the restrictive condition for carrying out ranging using monocular vision is more, so using binocular vision come this close to lane line
The target of distance carries out ranging accuracy rate high cost but also low not only, in general, develops a kind of based on deep learning and binocular vision
Lane detection, the distance measuring method of feel are necessary, and have very big potentiality and value in intelligent driving field.
Summary of the invention
The purpose of the present invention is to provide a kind of high cost of accuracy rate and low lane line distance measuring methods.Technical solution is such as
Under:
A kind of lane line distance measuring method based on deep learning and binocular vision: including image capture module, it is based on convolution
The lane detection module of neural network, the range finder module based on binocular vision.Wherein,
Image capture module is corresponding binocular camera, for acquiring the realtime graphic in vehicle travel process;
Lane detection module based on convolutional neural networks is used for accurately identifying the lane line positioned at vehicle two sides
Three layers after vgg16 network full articulamentum are become warp lamination, warp lamination pair by vgg16 network frame and full convolutional network FCN
The characteristic pattern of convolutional layer is up-sampled, to generate a prediction to each pixel, lane line forecasting problem is converted
It is solved to image pixel-class, obtains the binary map and instance graph of prediction lane line by convolutional neural networks, then after passing through
Reason and cluster show the lane line that neural network forecast goes out on the image;
Range finder module based on binocular vision, the lane line position that will have been obtained using Binocular Stereo Matching Algorithm SGBM algorithm
It sets, extracts the two sides lane line nearest point of camera of adjusting the distance and carry out depth calculation and realize real time distance;
The depth and distance of all the points in view can be obtained, it can lane line is obtained by the result of lane detection
On distance apart from camera closest approach;
Step 6: application can have been obtained by physical computing according to actual parameters such as vehicle width, camera installation sites
The distance of value.
It is of the present invention that tradition can solve based on deep learning and the lane detection distance measuring method of binocular ranging
The problems such as robustness of method for detecting lane lines is poor, real-time is poor, while the range finder module based on binocular vision is added, it can
To realize real-time and inexpensive lane detection and ranging, designed by the structure of high-precision neural network, in addition more scenes,
The continuous training study of a wide range of sample, it can be achieved that for lane detection precision up to 90% or more, and binocular ranging for
The measurement accuracy of short distance is very high, so that high-precision lane detection and ranging may be implemented again, for current intelligent driving
Field should be very helpful.
Detailed description of the invention
Fig. 1 convolutional neural networks vgg16+FCN structure
Lane detection and ranging effect picture of the Fig. 2 based on deep learning and binocular vision
Specific embodiment
The present invention includes: image capture module, the lane detection module based on convolutional neural networks, based on binocular vision
Range finder module.Wherein image capture module is corresponding binocular camera, real-time in vehicle travel process for acquiring
Image, optional left mesh or right mesh image are as display;Lane detection module based on convolutional neural networks is for accurately identifying
Lane line positioned at vehicle two sides, using vgg16 network frame and full convolutional network FCN, by three layers after vgg16 network full connection
Layer becomes warp lamination, and warp lamination up-samples the characteristic pattern of convolutional layer, so as to generate to each pixel
One prediction, is transformed into image pixel-class for lane line forecasting problem to solve, our available prediction lanes by network
The binary map and instance graph of line, then shown the lane line that neural network forecast goes out on the image by the work such as post-processing and cluster;
The lane line for being used to have obtained using traditional Binocular Stereo Matching Algorithm SGBM algorithm based on the range finder module of binocular vision
Position extracts the two sides lane line nearest point of camera of adjusting the distance and carries out depth calculation and realize real time distance.
Step 1: being demarcated to the binocular camera used, the internal reference of acquisition vision collecting module and outer ginseng.
Step 2: making the training set sample met with actual conditions in conjunction with actual conditions.Thus data disclosed in field
Collection Tusimple data set and Culane data set filter out nearly 15000 picture, and the size for being no headstock is 1280 × 720
Picture, the network of vgg16 and FCN are trained including daytime, more scene samples such as night, vehicle is crowded, vehicle two is sparse, is used
Large data sets are to obtain better robustness, reduce situations such as over-fitting.
Step 3: using the realtime graphic of image capture module acquisition vehicle driving, left and right view is intended to be stored up frame by frame
It deposits to carry out next step detection and ranging, as far as possible without the vehicle head part of vehicle when Image Acquisition, to guarantee the essence of detection and ranging
Exactness.
Step 4: optional left view or right view, predict each frame by trained lane detection module
Obtain the lane detection result of vehicle two sides.
Step 5: the binocular solid using binocular distance measurement module matches SGBM algorithm, all the points in view can be obtained
Depth and distance, it can the distance on lane line apart from camera closest approach is obtained by the result of lane detection.
Step 6: application can have been obtained by physical computing according to actual parameters such as vehicle width, camera installation sites
The distance of value.
Claims (1)
1. a kind of lane line distance measuring method based on deep learning and binocular vision: including image capture module, based on convolution mind
Lane detection module through network, the range finder module based on binocular vision.Wherein,
Image capture module is corresponding binocular camera, for acquiring the realtime graphic in vehicle travel process;
Lane detection module based on convolutional neural networks is used for accurately identifying the lane line positioned at vehicle two sides
Three layers after vgg16 network full articulamentum are become warp lamination, warp lamination pair by vgg16 network frame and full convolutional network FCN
The characteristic pattern of convolutional layer is up-sampled, to generate a prediction to each pixel, lane line forecasting problem is converted
It is solved to image pixel-class, obtains the binary map and instance graph of prediction lane line by convolutional neural networks, then after passing through
Reason and cluster show the lane line that neural network forecast goes out on the image;
Range finder module based on binocular vision, the lane line position that will have been obtained using Binocular Stereo Matching Algorithm SGBM algorithm,
The two sides lane line nearest point of camera of adjusting the distance is extracted to carry out depth calculation and realize real time distance;
The depth and distance of all the points in view can be obtained, it can by the result of lane detection obtain on lane line away from
With a distance from camera closest approach;
Step 6: application value can have been obtained by physical computing according to actual parameters such as vehicle width, camera installation sites
Distance.
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Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111309032A (en) * | 2020-04-08 | 2020-06-19 | 江苏盛海智能科技有限公司 | Autonomous obstacle avoidance method and control end of unmanned vehicle |
CN112613392A (en) * | 2020-12-18 | 2021-04-06 | 北京新能源汽车技术创新中心有限公司 | Lane line detection method, device and system based on semantic segmentation and storage medium |
CN115019278A (en) * | 2022-07-13 | 2022-09-06 | 北京百度网讯科技有限公司 | Lane line fitting method and device, electronic equipment and medium |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2018058356A1 (en) * | 2016-09-28 | 2018-04-05 | 驭势科技(北京)有限公司 | Method and system for vehicle anti-collision pre-warning based on binocular stereo vision |
CN109084724A (en) * | 2018-07-06 | 2018-12-25 | 西安理工大学 | A kind of deep learning barrier distance measuring method based on binocular vision |
CN109635744A (en) * | 2018-12-13 | 2019-04-16 | 合肥工业大学 | A kind of method for detecting lane lines based on depth segmentation network |
-
2019
- 2019-07-15 CN CN201910636651.2A patent/CN110472508B/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2018058356A1 (en) * | 2016-09-28 | 2018-04-05 | 驭势科技(北京)有限公司 | Method and system for vehicle anti-collision pre-warning based on binocular stereo vision |
CN109084724A (en) * | 2018-07-06 | 2018-12-25 | 西安理工大学 | A kind of deep learning barrier distance measuring method based on binocular vision |
CN109635744A (en) * | 2018-12-13 | 2019-04-16 | 合肥工业大学 | A kind of method for detecting lane lines based on depth segmentation network |
Non-Patent Citations (2)
Title |
---|
张新钰;高洪波;赵建辉;周沫;: "基于深度学习的自动驾驶技术综述" * |
林付春;张荣芬;刘宇红;: "基于深度学习的智能辅助驾驶***设计" * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111309032A (en) * | 2020-04-08 | 2020-06-19 | 江苏盛海智能科技有限公司 | Autonomous obstacle avoidance method and control end of unmanned vehicle |
CN112613392A (en) * | 2020-12-18 | 2021-04-06 | 北京新能源汽车技术创新中心有限公司 | Lane line detection method, device and system based on semantic segmentation and storage medium |
CN115019278A (en) * | 2022-07-13 | 2022-09-06 | 北京百度网讯科技有限公司 | Lane line fitting method and device, electronic equipment and medium |
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