A Looseness Detection Method for Railway Catenary Fasteners Based on Reinforcement Learning Refined Localization

• Published in: IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 13
• Main Authors: Zhong, Junping, Liu, Zhigang, Wang, Hongrui, Liu, Wenqiang, Yang, Cheng, Han, Zhiwei, Nunez, Alfredo
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Boxes; Component segmentation; Computer vision; Deep learning; Diagnosis; Fasteners; Feature extraction; Image segmentation; Inspection; Localization; Location awareness; looseness detection; Performance evaluation; Rail transportation; railway catenary fasteners; Reinforcement learning; reinforcement learning (RL)
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2021.3086913

Brace sleeve (BS) fasteners, i.e., nut and bolt, are small components but play essential roles in fixing BS and cantilever in railway catenary system. They are commonly inspected by onboard cameras using computer vision to ensure the safety of railway operation. However, most BS fasteners cannot be directly localized because they are too small in the inspection images. Instead, the BS is first localized for detecting the BS fastener. This leads to a new problem that the localized BS boxes may not contain the complete BS fasteners due to low localization accuracy, making it infeasible to further diagnose the fastener conditions. To tackle this problem, this article proposes a novel pipeline for BS fastener looseness diagnosis. First, the competitive deep learning model Faster RCNN ResNet101 is used to coarsely localize BSs. Second, an action-driven reinforcement learning agent is adopted to refine the coarse-localized boxes through a dynamic position searching process. Then, BS fasteners are extracted from the refined localized BS image by the deep segmentation model YOLACT++, which is fast and interpretable. Finally, a looseness diagnosis criterion based on segmented information are proposed. We evaluate the performance of submodels independently and the overall performance of the whole model on a real-life catenary image dataset collected from a high-speed line in China. The test results show that the proposed method is effective for BS looseness detection in railway catenary.