@inproceedings{6c8f924ad44a4db79c3720fb5c32837e,
title = "Bolt Loosening Detection for a Steel Frame Multi-Story Structure Based on Deep Learning and Digital Image Processing",
abstract = "Bolted joints are widely used in the field of aerospace, civil and mechanical engineering. During their service life, extreme loading or environmental factors can cause the loosening of bolts. In this paper, a bolt loosening detection method based on computer vision and image processing is developed to identify bolt rotation angle in a steel multi-story frame structure. The experimental results show that the bolt target detection accuracy can reach 100% by using the Yolo-V5s deep learning model trained with a self-developed bolt object dataset. The dataset consists of 337 bolt images captured in nature scenes. For the angle calculation, the final result shows that the identification error is less than 5.8°, and at a slight camera angle (0~20°), the maximum error even does not exceed 2.8°. Thus, the effectiveness of this method for detecting rotary loosening of bolts is well validated.",
keywords = "Bolt loosening, deep learning, image processing, rotation angle calculation, target detection",
author = "Yadian Zhao and Zhenglin Yang and Chao Xu",
note = "Publisher Copyright: Copyright {\textcopyright} 2022 by ASME.; ASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022 ; Conference date: 30-10-2022 Through 03-11-2022",
year = "2022",
doi = "10.1115/IMECE2022-94786",
language = "英语",
series = "ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)",
publisher = "American Society of Mechanical Engineers (ASME)",
booktitle = "Advanced Materials",
}