# A Vision-Based Bolt Looseness Detection Method for a Multi-Bolt Connection
## 1 Introduction
In 2021, they [14] used the same type of stress wave signal as the method in [11], but they developed two novel indicators: the multi-scale range entropy and the multi-scale bubble entropy of the signal as feature, to train an ensemble learning model for connection looseness detection. In the previous two works, the features still had to be selected by a human. In 2021, they [7] utilized newly developed convolutional long short-term memory networks for multi-bolt looseness detection. The convolution blocks in the networks can extract the proper features automatically, and the SoftMax layer can output the detection result. Although this method skipped the feature extraction by humans, it could not detect all the looseness cases compared to the method in Ref. [13]. Contacted sensor-based methods have undergone significant development over the years, yielding commendable results. Nonetheless, these methods demand direct physical contact with the measured objects, posing challenges in terms of feasibility and requiring the prior installation of specific sensors. This not only proves inconvenient but also entails additional costs. Furthermore, these approaches are vulnerable to environmental factors like temperature changes and humidity, potentially leading to inaccurate judgments. Vision-based methods are another major area of multi-bolt looseness detection. The advantage is their noncontact nature, and no pre-installed sensors are required. One of the basic vision-based approaches is to pre-mark the bolts and subsequently detect the angle of their rotation using the images captured by a camera. For example, [PERSON] et al. [15] marked the nut and bolt beforehand and used the fifth version of You Only Look Once (YOLOv5) to identify the position of the marks. The relative position change of the marks can be used to calculate the rotation angle of the nut and finally identify the loose one. But it will fail when the marker point is obscured. To solve this problem, [PERSON] et al. [16] utilized a brand new barcode mark (polarization-adjusted convolutional code, PAC-code) that covered the whole bottom surface of the bolt, which can avoid the failure caused by partial occlusion. In addition to the pre-marking method, researchers have explored many other vision-based methods to estimate their angles and detect the loose bolts. The Hough transform is a classic feature extraction algorithm, which can recognize the bolts' edges to estimate the bolts' angles [17; 18]. In 2015, [PERSON] et al. [18] combined the circular Hough transform with the Canny edge detector to recognize and segment each nut from the images. The method detected the loose bolt by calculating the rotation angles of the outlines of the bolt between the original image and the current image. But their experiment only considered the case in which the camera was facing the bolts. In 2020, [PERSON] et al. [19] built on [PERSON]'s work, they introduced the perspective transformation to solve the problems caused by a changed perspective angle. In 2019, [PERSON] et al. [17] developed a regional convolutional neural network (RCNN)-based algorithm to recognize and segment bolts in captured images. They also used the Hough transform to calculate the rotation angles of bolts just like the method in [19]. In 2021, they [20] designed a new faster RCNN algorithm combined with adaptive bolt-angle calculation for the multi-bolt connection on the Dragon Bridge in Vietnam. Compared to the quasi-automated method in [19], this system consisted of a complete set of automated inspection processes that greatly improved inspection efficiency. Experiments have proved that their methods can accurately detect loose bolts when the shooting angle is greater than 50\({}^{\circ}\). However, there are instances in which the Canny edge detector may struggle to detect the bolts' edges, especially when the contrast is low around the edges. The distortion rectification algorithm of the method is limited to rectangular-based bolt arrays.