Railway fasteners play a crucial role in connecting the track to the sleepers, and their performance is vital for the stability of the track structure and the safety of train operations. Traditional detection methods primarily rely on human experience, which can be inefficient and susceptible to human error. This paper proposes a novel method for railway fastener detection based on YOLOv8s. By incorporating the DAS dynamic convolution module and EMA attention mechanism modules, the method effectively filters out irrelevant background information, allowing the model to focus on critical areas within the image without increasing computational overhead. Experimental results indicate that the improved model demonstrates superior recognition performance for railway fasteners in complex environments. This research not only significantly improve the efficiency and accuracy of detecting the integrity of railway fasteners, but also ensure the safety and reliability of railway transportation.
Track Fastener Detection Method Based on Improved Yolov8s Algorithm
2024-11-15
995090 byte
Conference paper
Electronic Resource
English
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