Research on Preventive Maintenance Technology for Highway Cracks Based on Digital Image Processing

Cracks are the initial manifestation of various diseases on highways. Preventive maintenance of cracks can delay the degree of pavement damage and effectively extend the service life of highways. However, existing crack detection methods have poor performance in identifying small cracks and are unable to calculate crack width, leading to unsatisfactory preventive maintenance results. This article proposes an integrated method for crack detection, segmentation, and width calculation based on digital image processing technology. Firstly, based on convolutional neural network, a optimized crack detection network called CFSSE is proposed by fusing the fast spatial pyramid pooling structure with the squeeze-and-excitation attention mechanism, with an average detection accuracy of 97. 10%, average recall rate of 98. 00%, and average detection precision at 0. 5 threshold of 98. 90%; it outperforms the YOLOv5-mobileone network and YOLOv5-s network. Secondly, based on the U-Net network, an optimized crack segmentation network called CBUNet is proposed by using the CNN-block structure in the encoder module and a bicubic interpolation algorithm in the decoder module, with an average segmentation accuracy of 99. 10%, average intersection over union of 88. 62%, and average pixel accuracy of 93. 56%; it outperforms the UNet network, DeepLab v3+ network, and optimized DeepLab v3 network. Finally, a laser spot center positioning method based on information entropy combination is proposed to provide an accurate benchmark for crack width calculation based on parallel lasers, with an average error in crack width calculation of less than 2. 56%.