Traffic lane detection method and device based on depth learning multitask network

Abstract

The invention discloses a traffic scene lane line detection method and device based on depth learning multi-task network, Compared with the traditional lane detection algorithm based on straight linedetection, A deep learning multitask convolution neural network (CNN) is introduced, extracting lane characteristic information, As that detail information of each layer of the image is fully utilize,At first, that brightness and the edge information of the image are collect and evaluated, According to the evaluation results, the image is divided into several image blocks, then the image is normalized and sent to the depth learning network to output the lane image types and coordinates, and then the lane fitting is carried out by using the spatial image correlation, so as to realize the function of identifying the lane information accurately and quickly in different scenes and brightness. The invention is suitable for the application of bayonet camera and electronic police in the field ofintelligent transportation. On the premise of ensuring the real-time image analysis, the invention fully utilizes the depth learning network, and effectively improves the adaptability and accuracy ofthe lane line detection function.

Description

technical field [0001] The invention belongs to the field of intelligent video monitoring, and in particular relates to a method and device for detecting lane lines in traffic scenes based on a deep learning multi-task network. Background technique [0002] At present, the main method for lane line recognition in the industry is the Hough line detection method. The above method first needs to convert the color image into a grayscale image, which loses the color information in the image, and then performs binarization processing. In the process, it is inevitable A large number of edge details are lost, which further reduces the accuracy of detection. At the same time, there are obvious defects in the adaptability of different scene images, such as ground reflection after rain, low brightness at night, shadow coverage and vehicle occlusion. recognition of lane lines. Contents of the invention [0003] The purpose of the present invention is to provide a traffic lane detecti...

AI Technical Summary

Problems solved by technology

[0002] At present, the main method for lane line recognition in the industry is the Hough line detection method. The above method first needs to convert the color image into a grayscale image, which loses the color information in the image, and then performs binarization processing. In the process, it is inevitable A large number of edge details are lost, which further reduces the accuracy of detection. At the same time, there are obvious defects in the adaptability of different scene images, such as ground reflection after rain, low brightness at night, shadow coverage and vehicle occlusion. recognition of lane lines

Images