| Summary: | The catenary system is essential for ensuring the stable energy transmission of trains in high-speed railways. The non-contact catenary detection is a promising monitoring method, where the catenary image is captured by an industrial camera mounted on the inspection vehicle. The image quality is susceptible to the limitations of the environment and equipment, which adversely affects the catenary location and detection accuracy. In this paper, we propose an unsupervised learning-based catenary image enhancement method for improving localization accuracy. First, the enhancement model is optimized to enhance the catenary image quality, making it sharper and more conducive to detection. Subsequently, an advanced small target location approach, called TPH-YOLOv5, is used to locate the catenary components. Finally, we compared the localization performance of the enhanced image with the low-light image. The experiment results show that the proposed method can effectively enhance the quality of low-light catenary images and improve the positioning accuracy.
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