Task-aware meta-learning paradigm for universal structural damage segmentation using limited images

Abstract

Recently, structural damage recognition has gained significant progress using deep learning and computer vision techniques. However, the recognition accuracy highly relies on massive training images, the inter-class balance, and the completeness of different damage categories. In addition, the generalization ability for new damage categories and robustness under real-world scenarios are challenging. This study proposes a task-aware meta-learning paradigm using limited images for universal structural damage segmentation. First, an interpretable task generation strategy instead of random sampling is designed based on feature density clustering, and a synthetical metric of Jaccard distance and Euclidean distance is established to measure the feature similarity and discover the class separability in the high-level feature space. Second, a dual-stage optimization framework is built based on Model-Agnostic Meta-Learning (MAML), comprising an internal optimization of the inner semantic segmentation model and an external optimization of the meta-learning machine. Third, core samples around the cluster center are selected to form a query pool and evaluate the task-significance scores of different tasks within a meta-batch, which are utilized in the external optimization to control the orientation of gradient updates towards more significant tasks. Finally, a multi-type structural damage dataset, including concrete crack, steel fatigue crack, concrete spalling, cable corrosion, and cable clamp slipping, is utilized to verify the effectiveness and necessity. The results show that the segmentation accuracy better outperforms directly training the inner semantic segmentation model and the conventional MAML algorithm using fewer training images. The generalization ability for new structural damage is further verified.