Robust Industrial UAV/UGV-Based Unsupervised Domain Adaptive Crack Recognitions with Depth and Edge Awareness: From System and Database Constructions to Real-Site Inspections

The defect diagnosis of modern infrastructures is crucial to public safety. In this work, we propose a complete crack inspection system with three main components, including the autonomous system setup, the geographic-information-system-based 3D reconstruction, and the database construction as well as domain adaptive algorithms design. To fulfill the unsupervised domain adaptation (UDA) task of cracks recognition in infrastructural inspections, we propose a robust unsupervised domain adaptive learning strategy termed Crack-DA to increase the generalization capacity of the model in unseen test circumstances. Specifically, firstly, we propose leveraging the self-supervised depth information to help the learning of semantics. Secondly, we propose using the edge information to suppress the non-edge background objects and noises. Thirdly, we propose using the data augmentation-based consistency learning to increase the prediction robustness. Finally, we use the disparity in depth to evaluate the domain gap in semantics and explicitly consider the domain gap in the optimization of the network. Also, we propose a source database consisting of 11,298 crack images with detailed pixel-level labels for network training in domain adaptations. Extensive experiments on UAV-captured highway cracks and real-site UAV inspections of building cracks demonstrate the robustness and effectiveness of the proposed domain adaptive crack recognition approach.