Industrial UAV-Based Unsupervised Domain Adaptive Crack Recognitions: From Database Towards Real-Site Infrastructural Inspections

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 unsupervised domain adaptive 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 non-edge background objects and noises. We propose the data augmentation-based consistency to enhance the performance. More importantly, we proposed to use the disparity in depth to evaluate the domain gap in semantics and explicitly consider the domain gap in network optimization. A database composed of a large number of crack images with detailed pixel-level labels for network training is established. Extensive experiments on UAV-captured highway cracks and real-site UAV inspections of building cracks demonstrate the robustness and effectiveness of our proposed domain adaptive crack recognition approach.