重庆理工大学学报2026，Vol.40Issue(9)：10-18,9.DOI:10.3969/j.issn.1674-8425(z).2026.05.002
夜间场景中分心驾驶行为的轻量级目标检测与研究
The lightweight object detection and study of distracted driving behavior in nighttime scenarios
摘要
Abstract
Traditional models exhibit significant false positives and negatives when detecting distracted driving behaviors in nighttime environments.To address the issue,this paper develops an improved YOLOv11n optimization model,HCP-YOLOv11n(HGNet CPC PACM).First,YOLOv11n's backbone network is enhanced by incorporating the lightweight neural network architecture PP-HGNet(Paddle Paddle High Performance GPU Net),improving multi-scale feature extraction capabilities while reducing computational burden.Then,CPC(CSP partial convolution)is integrated with the C3k2 model to create C3k2_CPC,which is adopted as the model's head component.The integration enables more efficient extraction of effective features while maintaining low computational overhead.Finally,the PACM(pre-normalization adaptive gating channel enhancement multi-scale dilated)mechanism is proposed specifically for small object detection.By combining multiple dilation rates to expand the receptive field,this mechanism markedly enhances the network's performance in processing small targets and detailed behaviors.Compared to the baseline YOLOv11n model,the improved algorithm improves mAP50 by 3.4％,mAP50-95 by 1.2％,and Recall by 5.8％.Meanwhile,it reduces GFLOPs by 12.7％and parameter count by 24％.Additional experiments on Four Behaviors Dataset further verify the effectiveness of the model,demonstrating its exceptional performances in detecting distracted driving behaviors both in daytime and nighttime.关键词
分心驾驶/PP-HGNet/注意力机制/YOLOv11nKey words
distracted driving/PP-HGNet/attention mechanism/YOLOv11n分类
信息技术与安全科学引用本文复制引用
张瑞乾,袁旭浩,陈勇,秦慧军,周若轩..夜间场景中分心驾驶行为的轻量级目标检测与研究[J].重庆理工大学学报,2026,40(9):10-18,9.基金项目
国家自然科学基金面上项目(52077007) （52077007）
新能源汽车北京实验室建设项目(PXM2020_014224) （PXM2020_014224）
