Real-Time Pothole Segmentation Method of Asphalt Pavement Based on Perspective Transformation and CMSegNet

The regular detection of asphalt pavement damage is crucial for the safe operation of highway. However, the current detection method has the problem of low efficiency and accuracy. In addition, the image tangential distortion will be caused by the oblique photography of the acquisition equipment. Thus, the accuracy of extracted the pothole area is affected. In order to solve the above problems, a segmentation method of asphalt pavement pothole damage based on image correction and deep learning is proposed. Firstly, an image correction method called perspective change is proposed to solve the tangential distortion problem of asphalt pavement image. Secondly, an innovative detection and segmentation model called CMSegNet is developed and used for the first time for accurate segmentation of asphalt pavement pothole damage. In this model, the global feature extraction capability of the pothole target is improved by optimizing the Mamba structure. In addition, a Multi-Scale Attention Aggregation (MSAA) module is proposed to fuse multi-scale local and global features. The segmentation accuracy, IoU, F1-Score and segmentation efficiency of this model can reach 91.28%, 89.96%, 91.74% and 24.52FPS, respectively. Finally, on-site experiments of asphalt pavement pothole detection are carried out to verify the generality, power and scalability of the proposed correction method and segmentation model. The results show that the proposed image correction method based on perspective transformation can effectively reduce the error rate of quantization of pothole area. The error rate is only 6.84%, an improvement of 4.09%. In addition, the superiority of the proposed CMSegNet segmentation model is further proved.