Railway Infrastructure Detection Based on YOLOv8 with NPU Acceleration

Abstract This paper delves into the efficacy of utilizing the YOLOv8 model, which is based on a convolutional neural network (CNN), for the purpose of detecting objects within railway infrastructure, leveraging the capabilities of Neural Processing Units (NPU). It comprehensively explores various configurations of YOLOv8, each characterized by distinct architectural structures and input layer resolutions. These configurations were meticulously trained and evaluated using a sizable dataset comprising over 20 000 Full HD images. Through rigorous experimentation, this study elucidates the considerable potential of YOLOv8, especially when bolstered by NPU acceleration, in facilitating the real-time detection of objects within railway infrastructure. The performance of different YOLOv8 variants was thoroughly assessed by evaluating critical factors such as detection accuracy and computational efficiency. The findings of this research underscore the adaptability and resilience of YOLOv8 models across a spectrum of input resolutions, underscoring their proficiency in accurately identifying various elements of railway infrastructure under diverse environmental conditions. Furthermore, the integration of NPU acceleration emerges as a pivotal factor. It significantly augments the detection speed and responsiveness of the system, thereby enabling the swift processing of high-resolution images in real-time scenarios. This paper emphasizes the promising prospects associated with integrating YOLOv8 and NPU acceleration for applications in railway infrastructure monitoring and management. It offers valuable insights into the future trajectory of object detection technology within transportation systems, paving the way for enhanced efficiency and effectiveness in railway infrastructure operations.