Bolt loosening assessment is crucial to identify early warnings of structural degradation and prevent catastrophic events. This paper proposes an automatic bolt loosening assessment methodology. First, a novel end‐to‐end ensemble vision model, Bolt‐FP‐Net, is proposed to reason the locations of bolts and their hexagonal feature patterns concurrently. Second, an adaptive target‐free perspective correction method is proposed to correct perspective distortion and enhance assessment accuracy. Finally, an iterative bolt loosening quantification is developed to estimate and refine the bolt loosening rotation. Experimental parametric studies indicated that the proposed Bolt‐FP‐Net can achieve excellent performance under different environmental conditions. Finally, a case study was conducted on steel bolt connections, which shows the proposed methodology can achieve high accuracy and real‐time speed in bolt loosening assessment.

中文翻译：

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使用集成视觉模型进行螺栓松动评估，通过无目标透视自适应实现自动定位和特征提取


螺栓松动评估对于识别结构退化的早期预警和防止灾难性事件至关重要。本文提出了一种自动螺栓松动评估方法。首先，提出了一种新的端到端集成视觉模型 Bolt-FP-Net，以同时推理螺栓的位置及其六边形特征模式。其次，提出了一种自适应无目标透视校正方法，以校正透视失真并提高评估精度。最后，开发了一种迭代螺栓松动量化来估计和细化螺栓松动旋转。实验参数研究表明，所提出的 Bolt-FP-Net 在不同环境条件下都能取得优异的性能。最后，对钢螺栓连接进行了案例研究，结果表明所提出的方法可以在螺栓松动评估中实现高精度和实时速度。