Lane line damage detection algorithm based on YOLOv7 and image block
A lane line damage detection method combining YOLOv7 and image block is proposed.Firstly,YOLOv7 model is used to detect and extract the lane line area.Secondly,Otsu algorithm is used to calculate the threshold of each sub block and the gray mean difference between the background area and the target area in the sub block.The binarization is realized accordingly.Then,bilinear interpolation method is used to smooth the image and realize lane line segmentation,and the topological structure analysis method is used to extract the lane line contour.Finally,three methods including pixel statistics,straight line fitting and cutting detection are designed to judge whether the lane line is damaged.Experimental result shows that the precision of the algorithm is 91.79％ in lane line damage detection under different scenarios,which has good detection effect and certain application value.
lane line damage detectiondeep learningYOLOv7 algorithmblock segmentationOtsu method
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