Abstract: The detection and classification of concrete damage is essential for maintaining good infrastructure condition. Traditional semantic segmentation methods often can not provide accurate crack boundary information, which limits the further location and measurement analysis. In this study, the case segmentation method is used to solve the shortcomings of the previous detection methods and achieve more accurate crack identification results. This paper presents an improved YOLOv7 network design scheme. The network includes three different custom modules that can optimize the algorithm to solve missing feature problems, small recognition frames, and gradient problems, thereby improving accuracy. In addition, data sets with different sizes, exposures and noise are used to train the network, which expands the prediction range of the network and enhances the stability of the network. The experimental results show that compared with YOLOv7, YOLOv5, SOLOv2, Cascade Mask RCNN, Condinst, Sparseinst, mAP is significantly improved. Thus, the proposed network algorithm has high practical engineering value.
Keywords: Concrete cracking, Instance segmentation, Deep learning, Convolutional neural network, YOLOv7
Pavement crack instance segmentation using YOLOv7-WMF with connected feature fusion.pdf
