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A Segmentation-Driven Editing Method for Bolt Defect Augmentation and Detection
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DOI：10.1109/TSMC.2026.3655553.png)
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Abstract
En 中文
Bolt defect detection (BDD) is critical to ensure the safety of transmission lines. However, the scarcity of defect images and imbalanced data distributions significantly limit detection performance. To address this issue, we propose a segmentation-driven bolt defect editing (SBDE) method for defect generation and data augmentation. The proposed framework comprises three main components. First, a bolt attribute segmentation (BAS) model is constructed, which integrates contrast-based enhancement and frequency-domain filtering to improve edge feature representation. A multipart-aware optimization strategy is introduced to handle the noncontiguous structures of bolt attributes, enabling the generation of high-quality structural masks for subsequent editing. Second, a bolt attribute editing module based on image inpainting is designed, which removes specific bolt attributes by applying mask boundary optimization and context-aware reconstruction, thereby transforming normal bolts into defective ones. Finally, an editing recovery augmentation strategy is proposed to restore the edited bolts into the original inspection images, constructing complete defect samples for expanding the detection dataset. Extensive experiments were conducted on a self-built bolt dataset and the public MVTec AD dataset. The results show that the proposed method significantly outperforms existing state-of-the-art image editing approaches in generation quality and effectively improves defect classification and detection performance across multiple mainstream models, thereby validating the effectiveness and practical potential of the proposed approach. The code of the project is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Jay-xyj/SBDE</uri>
Keywords:
Bolt defect detection (BDD)
bolt defect generation (BDG)
data augmentation
defect attribute editing
segment anything model (SAM)
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