# A Vision-Based Bolt Looseness Detection Method for a Multi-Bolt Connection
## 5 Conclusions  
In this study, we proposed a precise, automated, and real-time method for detecting loose bolts in rigid multi-bolt connections. Firstly, the DSSPI system was established to capture shearing speckle patterns under different looseness cases. Secondly, fringe patterns were derived using the DSSPI subtraction algorithm. Thirdly, these fringe patterns were labeled and utilized for training the RNN model. In the actual detection of loose bolts, the pre-trained RNN classifier directly identifies the input fringe patterns to detect the loose bolts in multi-bolt connections. The method can detect all the bolts within the measured surface at one time, which is efficient. It achieved an impressive detection accuracy of over 99% and a quick completion time of 4.70 millisecond for a single judgment (multiple bolts at one detection), which is quite accurate and thousands of times faster than other methods in the literature. Compared to other vision-based method, the proposed method neither requires recognition and localization of the bolts from the original images previously nor does it require prior information such as the initial angle of the bolts. It is also not perturbed by overlapping bolt heads that mask other loosened bolts. It can also detect unseen bolts in multi-bolt connections, making it applicable for connections in complex structures in which occlusion often occurs. Furthermore, this method skips the process of distortion rectification and has a wide detection angle (at least from 90\({}^{\circ}\) to 30\({}^{\circ}\)). The trained RNN classifier maintains a detection accuracy over 99% at any perspective angle (90\({}^{\circ}\), 60\({}^{\circ}\), and 30\({}^{\circ}\)). Overall, the effectiveness, robustness to perspective angles, and robustness to occluded bolts of the method, which have been evidenced in the experiments, illustrate its great potential in multi-type connection looseness detection, even for connections in complex structures. Additionally, the method achieves real millisecond detection, which can be used in fast, real-time monitoring. That is a promising property of integrating it with webcams for the remote, real-time, and multi-scenario monitoring of connection looseness in future research. The application of the method enables bolt connections' lifecycle detection and management, thereby enhancing the accuracy and automation level of the detection work. Conceptualization, L.D. and Z.G. ; Data curation, M.L. ; Formal analysis, S.K. ; Funding acquisition, Z.G. ; Investigation, S.K. ; Methodology, L.D. ; Software, L.D. ; Supervision, Z.G. ; Validation, L.D., Y.S. and X.L. ; Visualization, L.D. ; Writing--original draft, L.D. ; Writing--review and editing, L.D. and Z.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, Grant Number 52075034. Not applicable. The data used to support the findings of this study are available from the corresponding author upon request. The data are not publicly available due to privacy. The authors declare no conflicts of interest. References  
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