Missing recognition of highway shading board based on deep convolution segmentation and correction

The lack of highway shading panels poses a major hidden danger to driving safety. It is urgent to study a method that can automatically detect the disease of the anti-glare panel and provide help for the maintenance of traffic safety auxiliary facilities. In the method for identifying the absence of shading panels on highways based on deep convolutional image segmentation and correction, the PointRend model based on deep convolutional networks (CNN) is first used to achieve the pixel-level fine segmentation of the shading plate area, and then the multiple images in the same image are segmented. A shading plate area, on the largest outer polygon estimated by the convex hull algorithm, the optimal outer quadrilateral is determined according to the distance between the vertices, and then the shading plate area correction is completed by affine transformation, and finally through the image one-dimensional projection mapping and adjacent shading The distance correlation between the boards realizes the identification and positioning of the missing light-shielding board. The highway shading plate missing recognition method based on deep convolution image segmentation and correction uses the vertex distance to quickly determine the external quadrilateral, which is suitable for estimating the shape of the area in a dynamic scene. After actual testing and verification, it can accurately and efficiently identify the disease of the anti-glare plate. Compared with traditional image segmentation methods, the method using the PointRend target segmentation model has better segmentation quality for target details, and it is more robust when dealing with background interference.