To ensure road safety,it is crucial to effectively identify road cracks.However,due to the complex and variable conditions of roads,most existing research still adopts supervised learning methods for crack detec-tion and localization.These methods are typically limited by the availability of datasets labeled with cracks,and manually annotating road images is not only time-consuming but also costly.In response to the above issues,this paper proposes a simple yet effective two-stage classification neural network based on self-supervised learning that uses only normal road surface data for image anomaly detection.Initially,abnormal data is generated by pro-cessing normal road surface data using the CutPaste module.Subsequently,features are extracted using the Resnet18 network enhanced through transfer learning,followed by normalization to produce classification re-sults.The trained network maintains its feature extraction capabilities through structural pruning,reducing the number of parameters by 74.8%.At the same time,compared to the network before pruning,the recognition accu-racy has only decreased by 1.7%.The trained Tiny-ResNet18 model has 2.97M parameters and achieved AUCs of 95.30%and 98.04%on the Crack Forest Dataset and Deep Crack dataset,respectively,demonstrating high rec-ognition precision.
维普
万方数据
