YOLO-VSI: An Improved YOLOv8 Model for Detecting Railway Turnouts Defects in Complex Environments

作     者：Chenghai Yu Zhilong Lu 

作者机构：School of Computer Science and TechnologyZhejiang Sci-Tech UniversityHangzhou310018China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第81卷第11期

页      面：3261-3280页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：We thank all the members who have contributed to this work with us. The authors received no specific funding for this study 

主　　题：YOLO railway turnouts defect detection mamba FPN(Feature Pyramid Network) 

摘      要：Railway turnouts often develop defects such as chipping,cracks,and wear during *** not detected and addressed promptly,these defects can pose significant risks to train operation safety and passenger *** advances in defect detection technologies,research specifically targeting railway turnout defects remains *** address this gap,we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex *** 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 backbone,proposed the C2f-VSS module to better capture long-range dependencies and contextual features,thus improving feature extraction in complex *** the network’s neck layer,we integrate SPDConv and Omni-Kernel Network(OKM)modules to improve the original PAFPN(Path Aggregation Feature Pyramid Network)structure,and proposed the Small Object Upgrade Pyramid(SOUP)structure to enhance small object detection ***,the Inner-CIoU loss function with a scale factor is applied to further enhance the model’s detection *** to the baseline model,YOLO-VSI demonstrates a 3.5%improvement in average precision on our railway turnout dataset,showcasing increased accuracy and *** on the public NEU-DET dataset reveal a 2.3%increase in average precision over the baseline,indicating that YOLO-VSI has good generalization capabilities.