Bolt Loosening Detection Method Based on Improved YOLOv8 and Image Matching

Bolt connections are widely used as structural connections in civil engineering, mechanical engineering, and bridge construction. However, problems such as loosening, or breakage can occur with bolts after prolonged use. To address the challenges of detecting bolt loosening, this study reviews existing detection technologies, analyzes their advantages and limitations, and proposes a novel bolt-loosening detection algorithm based on image matching and deep learning. The algorithm comprises the following components: a bolt target detection model based on an improved YOLOv8 algorithm, image correction using perspective transformation, bolt contour detection and image processing, and feature matching to calculate the transformation matrix between images obtained before and after loosening, thereby determining the loosening angle of the bolt. The experiments focused on a rectangular steel plate featuring four M6 standard bolts. The results demonstrate that the bolt target detection model can accurately locate and crop bolt positions and identify loosening angles under various shooting angles, distances, and lighting conditions. At specific shooting angles and appropriate distances, the detection error threshold was less than 2°. Subsequently, experiments conducted in real-world scenarios confirmed the accuracy and feasibility of the proposed algorithm.