Primitive detection method for station yard diagrams based on an improved YOLO11
LI Kaicheng
LI Xianglong
YUAN Lei
WEI Guodong
Abstract：In response to the challenge of extracting information from railway signal system station yard diagrams,this study proposes a primitive detection model,YOLO11-AT,based on an improved YOLO11.By constructing a detection model that integrates object detection and keypoint detection,it achieves automatic extraction of primitives and keypoints.First,an Attentional Scale Sequence Fu-sion(ASF)module is incorporated into the neck network to fuse multi-scale features,thereby enhanc-ing detection performance for small targets.Second,a Task-Aligned Dynamic Detection Head(TADDH)is implemented in the head network,which improves feature interaction between classifica-tion and localization tasks through task alignment,reduces feature conflicts,and increases detection accuracy for densely distributed targets.Finally,Slicing Aided Hyper Inference(SAHI)is applied to improve detection accuracy on high-resolution station yard images.Experiments are conducted on a constructed dataset containing multi-style station yard diagrams to validate the proposed method.The results show that,compared with YOLO11s-pose,the proposed YOLO11-AT improves precision,recall,mAP0.5,and mAP0.5-kp by 9%,2.2%,4.2%,and 3.2%,respectively,while reducing the number of parameters by 4.3%.Compared with existing mainstream detection models,YOLO11-AT achieves a better balance between detection accuracy and efficiency.The results indicate that the pro-posed method is adaptable to various styles of station yard diagrams and provide a feasible solution for automated information extraction from station yard drawings.
Keywords：station yardimage recognitionYOLO11keypoint detectiondrawing information ex-traction
Publication Date：2025-10-30
Online Publishing Date：2025-11-06(First online date of this platform, not the publication date of the document)
Pages：11( 198-208 )