Computer Vision-based Detection Method for Steel Bridge Bolt-looseness

In order to improve the intelligence of bolt-looseness detection, a computer vision-based detection method was proposed for steel bridge bolt-looseness. Firstly, bolt keypoint detection model was established based on deep learning theory to annotate the collected bolt images and to build datasets. Then the object detection model YoloV5 and the key-point detection model were trained separately to detect the bolt keypoints from top to bottom using the trained models. The location of bolt center points was determined according to the keypoints, and the perspective transformation matrix was solved according to the relative position of the center points, which was then used to reproject the keypoints. Finally, bolt-looseness was detected according to the position changes of keypoints. The results show that the trained YoloV5 model and keypoint detection model can accurately detect the keypoints of the bolts. The detection accuracy of the key-points is affected by the image acquisition conditions and is more sensitive to angles. Fitting the least-squares solution of the perspective transformation matrix using all center points can improve the accuracy of image geometry correction. The detection error of bolt-looseness under different image acquisition conditions ranges from 0% to 9. 6%, with a false detection rate of 2. 7%, indicating that the proposed method, with high accuracy and stability, has great practical value and broad engineering application prospects. © 2024 Science Press. All rights reserved.