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Neural computing-based multi-view vision system for precise bolt looseness detection
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AI
DOI：10.1080/13287982.2025.2588821.png)
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Abstract
En 中文
Maintaining the integrity of bolted joints is vital across engineering domains. Bolt loosening under dynamic loads can compromise structural safety. We present a robust neural approach using YOLOv11 for detecting loosening through pose-based estimation of nutbolt relative rotation. A dataset of 419 bolt images and 385 nut images supports training and evaluation. The method achieves an average relative angular error of 0.94 with prediction confidence up to 98%, and remains reliable under challenging illumination, with a maximum error of 3. This innovative approach offers a highly reliable and accurate tool for monitoring bolt loosening, even detecting minute rotational movements, thereby enhancing the safety and reliability of bolted connections in diverse engineering structures.
Keywords:
Bolt looseness detection
YOLOv11
pose estimation
structural reliability
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1.3
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25
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