An active learning method for crack detection based on subset searching and weighted sampling

作者：Zhengliang Xiang, Xuhui He, Yunfeng Zou, Haiquan Jing · 发表于：Structural Health Monitoring · 年份：2023 · DOI：10.1177/14759217231183661 · 被引用次数：10 · 研究领域：Infrastructure Maintenance and Monitoring、Concrete Corrosion and Durability、Non-Destructive Testing Techniques

Active learning is an important technology to solve the lack of data in crack detection model training. However, the sampling strategies of most existing active learning methods for crack detection are based on the uncertainty or representation of the samples, which cannot effectively balance the exploitation and exploration of active learning. To solve this problem, this study proposes an active learning method for crack detection based on subset searching and weighted sampling. First, a new active learning framework is established to successively search subsets with large uncertainty from the candidate dataset, and select training samples with large diversity from the subsets to update the crack detection model. Second, to realize the active learning process, a subset searching method based on sample relative error is proposed to adaptively select subsets with large uncertainty, and a weighted sampling method based on flow-based deep generative network is introduced to select training samples with large diversity form the subsets. Third, a termination criterion for active learning directly based on the prediction accuracy of the trained model is proposed to adaptively determine the maximum number of iterations. Finally, the proposed method is tested using two open-source crack datasets. The experimental comparison results on the Bridge Crack Library dataset show that the proposed method has higher calculation efficiency and prediction accuracy in crack detection than the un...