Chenghai Yu, Zhilong Lu*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3261-3280, 2024, DOI:10.32604/cmc.2024.056413
- 18 November 2024
Abstract Railway turnouts often develop defects such as chipping, cracks, and wear during use. If not detected and addressed promptly, these defects can pose significant risks to train operation safety and passenger security. Despite advances in defect detection technologies, research specifically targeting railway turnout defects remains limited. To address this gap, we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex environments. To enhance detection accuracy, we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU (YOLO-VSI). The model employs a state-space model (SSM) to enhance the C2f module in the YOLOv8… More >
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