Industrial UAV-Based Unsupervised Domain Adaptive Crack Recognitions: From Database Towards Real-Site Infrastructural Inspections
Kangcheng Liu, Ben M. Chen
Abstract
The defect diagnosis of modern infrastructures is crucial to public safety. In this work, we propose an unsupervised domain adaptive crack recognition framework. To fulfill the unsupervised domain adaptation (UDA) task of cracks recognition in infrastructural inspections, we propose a robust UDA learning strategy termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Crack-DA</i> to increase the generalization capacity of the model in unseen test circumstances. More specifically, we first propose leveraging the self-supervised depth information to help the learning of semantics. And then using the edge information to suppress nonedge background objects and noises. We also use the data augmentation-based consistency. More importantly, we use the disparity in depth to evaluate the domain gap in semantics and explicitly consider the domain gap in network optimization. A database consisting of 11 298 crack images with detailed pixel-level labels for network training in domain adaptations is established. Extensive experiments on unmanned aerial vehicle (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.