Abstract: In the automatic classification of the disease images of asphalt pavement, there are a great number of images with lane line, which is subject to interference. A method of lane line removal was proposed to reduce its impact on classification. Firstly, the detection model of the lane line region under complex background was trained based on the Mask R-CNN network, and the mask of the lane line region was automatically obtained through the model. Then the mask was used to completely remove all the lane line areas to get the damaged image. Finally, a modified Criminisi image inpainting method was used to fill the damaged image samples. Experiments show that the missed detection rate and the false detection rate are 0.50% and 7.87% respectively with the application of the Mask R-CNN method to detect the road image in 400 different environments. The improved Criminisi method enhances the repair speed by about 4 to 5 times than before under the premise of ensuring the quality of image restoration. Using VGG classification model for comparison verification, the new data set obtained after removing the lane line by the algorithm performs better under the same conditions.

Key words: lane line detection, Mask R-CNN, target removal, Criminisi, asphalt pavement