# Bolt Loosening Detection Using Key-Point Detection Enhanced by Synthetic Datasets
## 1 Introduction
Methods to locate the loose parts of bolted connections based on changes in the overall vibration data of the structure have also been proposed by many authors [10; 11]. The accuracy and reliability of these methods, which are achieved by the direct measurement of strain and stress, are quite high. However, the equipment used in these methods is often very expensive and is mainly used for experimental and special requirements, which makes it difficult to promote their use in practical engineering applications. With the rapid development of machine vision technology, more and more research is being conducted to apply machine vision to bolt loosening detection. Considering the special vision, according to the object of detection, these methods can be divided into two categories: relative rotation recognition and relative length recognition. Relative rotation recognition is generally the rotation angle recognition of the bolt or the nut, while relative length recognition is usually the length of the bolt above the fastener, as shown in Figure 1.  
Before the breakthrough of deep learning in the field of target detection, many researchers started to use traditional machine vision methods for bolt loosening detection. The detection methods mainly focus on bolt identification using a cascade bolt detector (CBD) [12], bolt or nut edge detection using Hough transform [13; 14], bolt loosening state classification using feature extraction and matching [15; 16], and bolt loosening state classification using support vector machines (SVM) [12; 15]. These methods are limited by  
Figure 1: Two categories of bolt loosening detection. (a) Relative rotation recognition; (b) Relative length recognition. technology and usually cannot balance the accuracy and robustness of recognition. Moreover, these traditional machine vision methods, such as Hough line detection, usually require long running times that preclude their application in real-time monitoring. What is more, it is difficult to identify bolts from images with complex backgrounds using these methods. Recently, with the rapid development of object detection convolutional neural networks (CNNs), such as Region-based CNN (RCNN) [17], Faster-RCNN [18], Mask RCNN [19], You Only Look Once (YOLO) [20], and Single Shot MultiBox Detector (SSD) [21], a much better bolt detection performance was achieved. Studies using CNN target detection algorithms to identify and extract individual bolt images followed by edge recognition using techniques such as the Hough transform to obtain bolt loosening angles are beginning to emerge [22; 23; 24]. Among them, [PERSON] et al. [23] used Solidworks to model bolts for expanding the dataset. Some research determines bolt loosening by making manual marks on the bolt and nut and identifying the relationship between the bolt and the marks by a target detection algorithm [25; 26; 27; 28; 29]. Some research qualitatively judges whether a bolt is loose by the length of the bolt extension [30; 31]. [PERSON] et al. [32] integrated YOLOv3-tini with an optical flow-based KLT tracking algorithm to achieve the real-time detection of bolt loosening. With the rapid development of face recognition and human pose recognition, key-point detection technology is also applied to bolt loosening recognition. The existing research is divided into two main directions: the identification of corner points of hexagonal bolts or nuts and the identification of overall key points of threaded fasteners. [PERSON] et al. [33] and [PERSON] et al. [34] used top-down and bottom-up approaches, respectively, to identify six corner points of a bolt and to detect bolt loosening by comparing the key point feature information of the same bolt at different times through two measurements. [PERSON] et al. [35] and [PERSON] et al.