One for all: Geometric structural-guided single domain generalization for vibration damper defect detection

Ke Yang, Zhedong Hu, Xiaoyu Zhao, Qianming Wang, Dongyang Hu, Yongjie Zhai

Abstract: Improving the generalization capabilities of deep learning-based vibration damper defect detection models is crucial for their applicability in complex power scenarios. Existing models struggle to cope with scenario data not encountered during training, including diverse geographical regions, weather conditions, and lighting scenarios. To address these challenges, this paper introduces a novel single domain generalization(DG) method for vibration damper defect detection, aimed at enhancing the generalization capabilities of current models. Under analyzing the structural and morphological characteristics of vibration damper defects, this study selects their geometric structural features as domain invariant features(DIF), guiding model training with artificial samples generated via three dimensional(3D) modeling. To ensure comprehensive learning of the DIF of vibration damper defects, this study employs multi-scale contrastive learning(MCL) loss and multi-morphology similarity metric learning(MSM) loss during the feature extraction and candidate box processing stages, respectively. These approaches guide the model in extracting geometric features and achieving consistent expression of defect characteristics. The experimental results demonstrate that the proposed method outperforms advanced object detection models in DG for vibration damper defect detection, achieving an average DG accuracy of 68.6% across multiple target domains and a sensitivity of only 0.219, thereby realizing the “one-time training, everywhere application” concept in vibration damper defect detection models.

External IDs:dblp:journals/eaai/YangHZWHZ25