重庆理工大学学报2025，Vol.39Issue(17)：23-30,8.DOI:10.3969/j.issn.1674-8425(z).2025.09.003
复杂道路场景中的感受野优化目标检测研究
Research on receptive field optimization object detection in complex road scenes
摘要
Abstract
To address the insufficient detection and weak generalization of small and occluded targets in complex traffic environments,an optimized model based on YOLOv8,named YOLOv8-MS,is developed.First,a lightweight receptive field enhancement module MGSmodule is proposed to optimize the C2f module,improving the efficiency and accuracy of feature extraction.Then,the separation and enhanced attention module SEAM is introduced,which effectively focuses on the areas affected by the concentration of the group,thereby improving the model's detection of small targets.Finally,a downsampling feature extractor is specifically designed for small targets,aiming to reduce the false detection and missed detection rates of small targets and further improve detection accuracy.Verified on the KITTI dataset,the improved algorithm improves P,R,mAP50,and mAP50-95 compared to the benchmark model YOLOv8n by 0.9％,6.3％,5.7％,and 4.8％respectively.Meanwhile,the model is validated on the VisDrone dataset.It improves P,R,mAP50,and mAP50-95 by 2.5％,2.3％,2.6％,and 1.6％respectively,demonstrating fairly good generalization and robustness.关键词
自动驾驶/小目标检测/注意力机制/YOLOv8nKey words
autonomous driving/small target detection/attention mechanism/YOLOV8n分类
信息技术与安全科学引用本文复制引用
张瑞乾,秦慧军,陈勇,袁旭浩,周若轩..复杂道路场景中的感受野优化目标检测研究[J].重庆理工大学学报,2025,39(17):23-30,8.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）
