Complex traffic scene detection method based on YOLO-NPDL
ZHANG Haochen
ZHANG Zhulin
SHI Ruiyan
CAO Shijie
WANG Wenhan
LEI Zhennuo
Abstract：In order to improve the detection accuracy of the vehicle object detection model in complex traffic scenes,using YOLOv8n(you only look once version 8 nano)as the benchmark model,a Neck-ARW(including auxiliary detection branch,RepBlock module,and weighted jump feature fusion)neck structure with a composite backbone is designed to reduce information loss caused by information bottlenecks along the network depth direction;the RepBlock structure heavy parameterization module is introduced,and the multi-branch structure is used in the training process to improve the model feature extraction performance;the P2 detection layer is added to capture more small target detail features and enrich the feature information flow of small targets in the network;the Dynamic Head self-attention mechanism detection head is used,which integrates scale perception,spatial perception,and task perception self-attention mechanism into a unified framework to improve detection performance.The layer-adaptive magnitude based pruning(LAMP)algorithm is used to remove redundant parameters of the model and construct the YOLO-NPDL(Neck-ARW,P2,Dynamic Head,LAMP)vehicle object detection model.Using the UA-DETRAC(university at Albany detection and tracking)dataset as the experimental dataset,RepBlock module embedding position test,different neck structure comparison test,pruning test,ablation test,and model performance comparison test are conducted to verify the detection accuracy of the YOLO-NPDL model.The experimental results show that:RepBlock module has better feature extraction ability for multi-scale targets when embedding auxiliary detection branches and neck trunk structures at the same time,and can retain more detailed information during the training process,but the amount of parameters and computation increases;after using the Neck-ARW neck structure,the detection accuracy indicators EmAP50 and EmAP50-95 of the model are increased by 1.1％and 1.7％,respectively,and the number of parameters is reduced by about 17.9％,and the structure is better;when the pruning rate is 1.3,the model parameters and computation are reduced by about 38.0％and 24.0％,respectively,and the redundant channel accounts for less,and the structure is more compact;compared with the YOLOv8n model,the YOLO-NPDL model has a 2.7％increase in recall rate,a 2.7％increase in EmAP50,reaching 94.7％,a 6.4％increase in EmAP50-95,reaching 79.7％;compared with the widely used YOLO series models,the YOLO-NPDL model has higher detection accuracy on the basis of fewer parameters.The YOLO-NPDL model has no obvious false detection or omission in detecting remote targets,rainy days and night scenes,and can detect more remote small target vehicles,with better detection effect.
Keywords：object detectioncomplex traffic sceneYOLOv8nNeck-ARWRepBlockLAMP algorithm
Publication Date：2025-03-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：14( 34-47 )