基于分割的螺栓缺陷增强与检测方法
A Segmentation-driven Editing Method for Bolt Defect Augmentation and Detection
AI总结：
本文提出基于分割的螺栓缺陷增强与检测方法，通过属性分割和编辑技术生成缺陷图像，提升检测性能。
摘要：
螺栓缺陷检测对于确保输电线路的安全性至关重要。然而，缺陷图像的稀缺性和数据分布不平衡显著限制了检测性能。为了解决这个问题，我们提出了一种基于分割的螺栓缺陷编辑方法（SBDE）来增强数据集。首先，提出了一种螺栓属性分割模型（Bolt-SAM），通过CLAHE-FFT适配器（CFA）和多部分意识掩码解码器（MAMD）增强了复杂螺栓属性的分割，生成高质量的掩码用于后续的编辑任务。其次，设计了一个掩码优化模块（MOD）并将其与图像修复模型（LaMa）结合，构建了螺栓缺陷属性编辑模型（MOD-LaMa），通过属性编辑将正常螺栓转换为缺陷螺栓。最后，提出了一种编辑恢复增强（ERA）策略，将编辑后的缺陷螺栓恢复并放回原始检查场景中，扩展缺陷检测数据集。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很大程度上优于最先进的图像编辑模型，有效提高了螺栓缺陷检测的性能，充分验证了所提出方法的有效性和应用潜力。该项目的代码可在https://github.com/Jay-xyj/SBDE上获得。
英文摘要：
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 https://github.com/Jay-xyj/SBDE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
