Bolt loosening assessment using ensemble vision models for automatic localization and feature extraction with target‐free perspective adaptation

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.