Research on Image Segmentation Methods of Highway Pavement Distress Based on Semantic Segmentation Convolutional Neural Network

Aiming at the problem that the image segmentation accuracy of highway pavement distress is easily affected by complex texture, noisy background, uneven illumination conditions and external environmental interference, this paper studies the image segmentation methods of highway pavement distress based on semantic segmentation Convolutional Neural Networks (CNN). Firstly, the methods of the image segmentation highway pavement distress based on FCN-DenseNet, DeepLabv3+, MobileNet are compared and analyzed. Secondly, the four variants of CNN models are investigated for the image segmentation of highway pavement distress, including FCN-DenseNet121 for Pavement Distress Segmentation (FCN-D121-PDS), DeepLabv3-DRN for Pavement Distress Segmentation (DL-D-PDS), DeepLabv3-MobilenetV3 for Pavement Distress Segmentation (DL-M-PDS and DeepLabv3-Mobilenet1 for Pavement Distress Segmentation (DL-MI-PDS). Finally, the comparative experiments were conducted, and the results showed that the average of DL-M1-PDS network is superior to the other three methods, with image segmentation accuracy of 98.20%.