基于改进YOLOv8的接触网异物检测模型研究OA

To address the problems of insufficient feature extraction,severe background interference,and low detec-tion accuracy in existing catenary foreign object detection methods,we propose an improved YOLOv8-based detec-tion model.First,a C2f-SCConv module is introduced into the feature extraction backbone of YOLOv8 to enhance the model's adaptability to foreign object features and background variations.Then,we embed a BiFPN structure to effectively fuse multi-scale feature information,thereby improving detection accuracy for objects of different scales.Furthermore,a GAM mechanism is incorporated to strengthen image noise suppression and enhance model robust-ness.Finally,the Wise-IoU loss function is introduced to increase sensitivity to complex object boundaries and en-hance fine-grained feature capabilities.Experimental results demonstrate that the improved model achieves a mean Average Precision of 89.9％in catenary foreign object detection,significantly improving the detection performance for small targets and meeting the practical requirements of catenary foreign object inspection.