Abstract
Bolt defect detection 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 problem, we propose a segmentationdriven bolt defect editing method (SBDE) to augment the dataset. First, a bolt attribute segmentation model (Bolt-SAM) is proposed, which enhances the segmentation of complex bolt attributes through the CLAHE-FFT Adapter (CFA) and Multipart- Aware Mask Decoder (MAMD), generating high-quality masks for subsequent editing tasks. Second, a mask optimization module (MOD) is designed and integrated with the image inpainting model (LaMa) to construct the bolt defect attribute editing model (MOD-LaMa), which converts normal bolts into defective ones through attribute editing. Finally, an editing recovery augmentation (ERA) strategy is proposed to recover and put the edited defect bolts back into the original inspection scenes and expand the defect detection dataset. We constructed multiple bolt datasets and conducted extensive experiments. Experimental results demonstrate that the bolt defect images generated by SBDE significantly outperform state-of-the-art image editing models, and effectively improve the performance of bolt defect detection, which fully verifies the effectiveness and application potential of the proposed method. The code of the project is available at this https URL.
Abstract (translated)
螺栓缺陷检测对于确保输电线路的安全至关重要。然而，由于缺乏缺陷图像和数据分布不平衡的问题，限制了检测性能的提升。为了解决这个问题，我们提出了一种基于分割驱动的螺栓缺陷编辑方法（SBDE），用于扩充数据集。首先，提出了一个螺栓属性分割模型（Bolt-SAM）。该模型通过CLAHE-FFT适配器（CFA）和多部件感知掩码解码器（MAMD）增强了复杂螺栓属性的分割能力，生成高质量的掩码以供后续编辑任务使用。其次，设计并集成了一种掩码优化模块（MOD），并与图像修复模型（LaMa）结合构建了螺栓缺陷属性编辑模型（MOD-LaMa）。该模型通过属性编辑将正常螺栓转换为有缺陷的螺栓。最后，我们提出了一种编辑恢复增强（ERA）策略，用于恢复并重新放入经过编辑后的缺陷螺栓到原始检查场景中，并扩展缺陷检测的数据集。 我们构建了多个螺栓数据集，并进行了广泛的实验。实验结果表明，SBDE生成的螺栓缺陷图像在性能上显著优于现有的图像编辑模型，有效提升了螺栓缺陷检测的表现力，充分验证了该方法的有效性和应用潜力。该项目的代码可以在提供的链接处获取。
URL
https://arxiv.org/abs/2508.10509
