Affiliations: [a] Research Institute of Highway, Ministry of Transport, Beijing, China
| [b] College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China
Correspondence:
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Corresponding author. Rufei Liu, College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China. Tel.: +86 13969708370; E-mail: liurufei@sdust.edu.cn.
Abstract: How to quickly and accurately identify the bridge rubber bearing deterioration plays an important role in ensuring the bridge structure and road safety. This paper selects the common rubber bearings of domestic bridges as the research object, and proposes an improved YOLOv4-based bridge rubber bearing deterioration detection algorithm to address the reasons for the difficulty in detecting bridge rubber bearing deterioration due to large scale variations and small sample data sets. An image dataset (named HRBD) with annotations is constructed from real inspection scenarios, and the data is expanded by image processing means such as rotation, translation and brightness transformation, so that this dataset has sufficient data complexity and solves the problem of overfitting due to insufficient samples for network training. The anchor applicable to this dataset was regained by the K-means++ clustering algorithm, and then the CA module was inserted into the YOLOv4 backbone network for more accurate anchor localization. The improved YOLOv4 network was used for migration learning to train the dataset, and finally the trained network model was used for detection on the test set. The experimental results show that the improved YOLOv4 bridge rubber bearing deterioration detection and identification network can effectively identify and locate bridge rubber bearings and their deterioration types (crack damage, shear deformation, bearing void).
