基于ADF-YOLO模型的双模态轨道扣件缺陷检测方法OA

In rail fastener defect detection,single-modal visible-light images are susceptible to environmental interference and lack spatial structural information.This limitation reduces the model's sensitivity to small-sized defects and spatial deformations of fasteners.Consequently,it increases the rate of false and missed detections.To address these issues,this paper proposes a detection method based on the Attention and Dual-Modal Fusion YOLO(ADF-YOLO)model.First,the ADF-YOLO model is constructed,which employs a dual-stream backbone architecture to concurrently extract texture features from visible-light images and spatial structural features from depth images of railway fasteners.Second,an Attention and Multi-Branch Convolution Synergy Module is introduced into the backbone network to improve the capture of small-sized defect features.This module expands the receptive field and enhances the representation of detailed features.Finally,an Attention-Guided Dual-Modal Feature Fusion Module is integrated into the neck network for the efficient fusion of heterogeneous features.This module fully exploits the dual-modal feature fusion paradigm.Consequently,it generates more robust fused features that are highly sensitive to defect morphology.The results demonstrate that on a multimodal fastener defect dataset,the ADF-YOLO model detection method achieves a mean average precision(mAP)of 90.9%and 70.5%at IoU thresholds of 0.5 and 0.5-0.95,respectively.The ADF-YOLO method significantly outperforms various single-modal and multi-modal object detection models in detection accuracy.The proposed ADF-YOLO model provides reliable technical support for railway fastener defect inspection tasks.