Road Crack Detection Based on Abnormal Data Generation and Self-supervised Learning
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.
万方数据
维普
