期刊
AUTOMATION IN CONSTRUCTION
卷 131, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.autcon.2021.103913
关键词
Deep convolutional neural network (CNN); High-speed railway; Catenary support devices (CSDs); Fastener; Anti-bird cover; Defect detection
资金
- National Natural Science Foundation of China [91738301, I20D00010]
The study introduces an innovative two-stage method utilizing two improved convolutional neural network-based networks, and outperforms traditional methods in terms of accuracy and processing speed.
In high-speed railways, contact forces between the pantograph and the overhead catenary system (OCS) are substantial. Vibration and excitation propagated from the vehicle-track and the pantograph-OCS interactions would progressively damage fasteners, including but not limited to, loose bolts, cracked components, and missing parts. Existing automatic detection methods typically rely on a three-stage approach, of which the first two stages focus on locating joints and fasteners while the last stage focuses on the detection. Due to the nature of the three-stage detector, the computational cost is high, and the inspection speed is low. This study proposes an innovative two-stage method with two improved convolutional neural network (CNN)-based networks, cascade YOLO (You Only Look Once) and Rotation RetinaNet (RRNet). The proposed method was compared to traditional horizontal anchor-based methods and other methods. The results demonstrate the proposed method outperforms other methods in terms of accuracy, while maintaining a reasonably high processing speed.
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