Nondestructive testing of bridge cracks based on transfer learning

In order to solve the problems of sample scarcity, environmental noise interference and lack of generalization ability of traditional models in bridge crack detection, a two-mode nondestructive testing method based on transfer learning was proposed in this study. By constructing the cooperative framework of feature transfer layer, model transfer layer and relation transfer layer, the cognitive limitation of traditional isolated learning is broken through, and cross-domain knowledge transfer and feature space reconstruction are realized. At the method level, the dual verification system of VGG19-based migration detection network and improved DeeplabV3+ segmentation model is established. The former reuses ImageNet pre-training features through parameter sharing mechanism, and realizes crack classification detection combined with fine-tuning strategy. The latter innovatively integrates high-efficiency channel attention and space-channel collaborative attention modules to build ResNet-50 optimized encoder-decoder architecture to improve fine-grained feature extraction capabilities. The experimental results show that the VGG19 migration model achieves F1 value of 0.983 on the test set, the MIoU of the improved DeeplabV3+ reaches 93.30%, and the reasoning time is controlled within 68.5ms. In this study, a two-stage transfer learning mechanism is used to effectively solve the problem of source domain and target domain distribution migration, which provides a high-precision and low-delay intelligent detection paradigm for bridge health monitoring in complex engineering environments.