SummaryVision‐based bolt defect detection methods based on feature changes have been reported. However, the robustness of key feature extraction and bolt detection requires improvement. This paper proposes a robust missing and loose bolt defect detection approach. The key features—reference points for perspective correction and the straight lines of the bolt edges—are extracted from the masks obtained by semantic segmentation models. The true and false bolt discrimination approach based on the mask shape can help improve bolt object detection accuracy. Overlapping between the bolt bounding boxes in the reference and detection images indicates missing bolts. The rotation angles reveal loosened bolts. The proposed approach was tested on fabricated bolted joint specimens and a steel railway bridge. The results suggest that these improvements ensure defect detection accuracy, with a miss rate of only 1% for missing bolt detection. Moreover, a loosened bolt with only 3° rotation is successfully detected. This approach has promising potential applicability in automatically detecting bolt defects in large steel structures.

中文翻译：

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基于视觉的拼接板接头鲁棒缺失和松动螺栓检测

摘要基于特征变化的基于视觉的螺栓缺陷检测方法已经被报道。然而，关键特征提取和螺栓检测的鲁棒性需要改进。本文提出了一种稳健的螺栓缺失和松动缺陷检测方法。关键特征——透视校正的参考点和螺栓边缘的直线——是从语义分割模型获得的掩模中提取的。基于掩模形状的真假螺栓判别方法有助于提高螺栓目标检测的准确性。参考图像和检测图像中的螺栓边界框之间的重叠表示缺少螺栓。旋转角度显示螺栓松动。所提出的方法在预制的螺栓接头样本和钢制铁路桥上进行了测试。结果表明，这些改进确保了缺陷检测的准确性，缺失螺栓检测的漏检率仅为 1%。而且，仅旋转3°的螺栓松动就被成功检测到。该方法在自动检测大型钢结构中的螺栓缺陷方面具有广阔的潜在应用前景。