Anti-interference and small object detection method for road scene based on RSG-YOLOv10n
KONG Feiyi
FU Zhenshan
WANG Yugang
FU Cong
DAI Xianxin
MA Dong
Abstract：Addressing the issues of background interference,distant small objects,and feature loss in autonomous driving road scene object detection,an improved model based on You Only Look Once version 10n(YOLOv10n)is proposed.The receptive field attention convolution(RFAConv)module is introduced to replace the Conv module,extracting multi-scale receptive field spatial features and dynamically allocating weights through attention mechanisms to enhance the model's complex image processing capabilities.A small object enhance pyramid(SOEP)module is incorporated,utilizing an improved cross stage partial-omniKernel(CSP-OmniKernel)module for feature information integration,significantly improving small object detection performance.The global channel-spatial attention(GCSA)module is introduced to enhance feature map representation through the coupling mechanism of channel attention,channel shuffle,and spatial attention,capturing global dependencies in feature maps and enhancing feature extraction capabilities,forming the RSG-YOLOv10n complex road scene small object detection model.Ablation experiment,model performance comparison experiment,generalization validation experiment,and visualization detection effect experiment are conducted.Experimental results show that:after introducing the RFAConv,SOEP,and GCSA modules,the RSG-YOLOv10n model's precision P,recall R,mean average precision at 50 intersection over union threshold EmAP50,and mean average precision averaged over intersection over union thresholds from 50 to 95(with a step of 5)EmAP50-95 is improved by 6.3 percentage points,2.5 percentage points,3.5 percentage points,and 2.8 percentage points respectively compared to the YOLOv10n model,with significantly enhances detection accuracy;compared with lower parameter models(YOLOv5n,YOLOv8n,YOLOv10n),similar parameter models(YOLOv3-tiny,YOLOv6n,YOLOv7-tiny),and higher parameter model(RT-DETR-L),the RSG-YOLOv10n model achieves the highest detection accuracy with lower parameter count and floating-point operations;the RSG-YOLOv10n model performs road scene object detection on a generalization validation dataset composed of various adverse weather images including sandstorms,heavy snow,and dense fog,with EmAP50 and EmAP50-95 increasing by 0.9 percentage points and 1.3 percentage points respectively compared to the YOLOv10n model,demonstrating high robustness and generalization capability;the RSG-YOLOv10n model exhibits strong feature extraction and small object detection capabilities in visualization detection experiments for road scenes with dense occlusion,complex lighting,adverse weather,and coexisting near and distant objects.
Keywords：small object detectionnbackground interferencefeature lossRSG-YOLOv10nattention mechanism
Publication Date：2025-09-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：12( 90-101 )