摘要
Abstract
This paper proposed a rail damage detection method based on an improved EfficientDet and YOLOv7 algorithm to address the issues of low efficiency and insufficient accuracy in traditional manual inspections.The method combined EfficientNet with the bi-directional feature pyramid network(BiFPN),significantly enhancing the processing ability of multi-scale features.Additionally,deeper convolutional layers were introduced to strengthen feature extraction capabilities.Using high-definition cameras and lighting compensation devices to capture images of the rail surface,the convolutional neural network processed and analyzed the images to achieve rapid and accurate rail damage detection.Experimental results show that,compared to YOLOv7,YOLOv5,and YOLOv3,the EfficientDet-YOLOv7 algorithm improves the mean average precision(mAP),precision,and recall by 9.27％,8.50％,and 9.20％,respectively,while also offering higher computational efficiency and faster convergence speed.关键词
钢轨损伤检测/图像采集/YOLOv7/EfficientDet/多尺度特征处理Key words
Rail Damage Detection/Image Capture/YOLOv7/EfficientDet/Multi-scale Feature Processing分类
交通工程
