信息与控制2025，Vol.54Issue(4)：632-643,12.DOI:10.13976/j.cnki.xk.2024.2082
复杂交通场景下的目标检测方法
Object Detection Method in Complex Traffic Scenarios
摘要
Abstract
To address the limitations of target detection methods in complex traffic scenarios,particularly missed detections of small and occluded targets and challenges in multi-scale target detection and model robustness,we propose an improved YOLOv8s-SRCEM model.We introduce a small target detection head to enhance the sensitivity of the model to small-sized targets,improving its detection capability for such objects.Additionally,we integrate the Res-CBAM attention module into the small target detection head to further enhance feature learning salience.We incorporate the ECA module into the backbone network to strengthen the attention to important feature channels of the model and improve feature selection and model robustness.Furthermore,by replacing the original SPPF module with MS-Block,we enhance the feature capture and fusion capabilities of the model across different scales.On the KITTI dataset,the improved model achieves a 6.6％increase in mAP compared to the YOLOv8s model.Experimental results demonstrate that the combination of these en-hancements substantially improves the detection performance of the model in complex traffic scenarios.关键词
复杂场景/小目标检测/MS-Block(Multi-Scale Block)/ECA(Efficient Channel Atten-tion)/注意力模块Key words
complex scenario/small target detection/MS-Block/ECA/attention module分类
信息技术与安全科学引用本文复制引用
濮志远,罗素云..复杂交通场景下的目标检测方法[J].信息与控制,2025,54(4):632-643,12.基金项目
国家自然科学基金项目(62101314) （62101314）
