Task-aware meta-learning paradigm for universal structural damage segmentation using limited images. (1st June 2023)

• Record Type: Journal Article
• Authors: Xu, Yang; Fan, Yunlei; Bao, Yuequan; Li, Hui
• Is Part Of: Engineering structures. Volume 284(2023)
• Publication Date: 2023-06-01
• Subjects: Multi-type Structural Damage Recognition -- Semantic Segmentation -- Meta-Learning -- Task Significance -- Limited Images; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2023.115917
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A task-significance-aware meta-learning paradigm is proposed for multi-type structural damage segmentation. An interpretable task generation strategy is designed by high-dimensional feature density clustering using a refined Jaccard distance. A task-significance-aware mechanism is introduced into meta-learning using a standard query pool. The proposed method outperforms directly training a recognition model and conventional MAML using only a few images. Ablation experiments and comparative studies are performed to further demonstrate the effectiveness, necessity, generalization ability, and robustness. 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.