Multiscenario Generalization Crack Detection Network Based on the Visual Foundation Model

Abstract

Recently, convolutional neural networks (CNNs) and hybrid networks, which integrate CNN with Transformer, have been widely employed in structuring crack detection, effectively addressing the challenges of high-precision crack identification in controlled scenes. However, scene generalization remains a significant challenge for existing networks, especially under limited dataset conditions. With the rapid development of foundation models (like ChatGPT), achieving scene generalization has become feasible. In this paper, by taking tunnel crack detection as the background, the CraSAM network is proposed, which incorporates a foundation model-based encoder and a prompt transfer learning module. Based on six datasets including tunnel, bridge, building, and pavement, the CraSAM is compared with 15 state-of-the-art models, including Unet, DeepLabv3+, SSSeg, and TransUNet. It exhibits superior generalization capability both on few-sample learned and unlearned conditions. This work will benefit to investigate of new ways for the utilization of the visual foundation model in various professional fields.