Quantitative loosening detection of threaded fasteners using vision-based deep learning and geometric imaging theory

Abstract

With the development of signal processing and deep learning, vision-based methods for loosening detection have progressed in recent years. However, existing visual inspection methods are mainly used to measure the loosening angle, which requires prior knowledge of the initial tightening states, and is only applicable to hexagon bolts and nuts with clear edges. This paper proposes a method to quantitatively calculate the length of the exposed bolt for detecting loosening using vision-based deep learning and geometric imaging theory. The newly proposed method consists of three modules: the region of interest (RoI) location, keypoint detection, and length calculation modules. The RoI location module applies a faster regional convolutional neural network (Faster-RCNN)-based deep learning algorithm to locate the exposed bolt, whereas the keypoint detection module employs a cascaded pyramid network (CPN)-based deep learning algorithm to detect five keypoints on the exposed bolt. An accurate expression is derived from the geometric imaging theory, and the coordinates of the five keypoints and camera parameters are imported into the expression to calculate the length of the exposed bolt. In the experiment, the resolution of input image was 640 × 640 pixels, and the test speed of the proposed method was approximately 200 milliseconds per image. Experimental results showed an average measurement error of 0.61 mm using our method, outperforming other measurement methods and the state-of-the-art networks of human pose estimation. Moreover, the robustness of the proposed method was validated by varying the shooting conditions, bolt sizes and colours. Finally, some factors influencing detection and measurement accuracies are discussed, and prospects for further research are proposed.