电讯技术2025，Vol.65Issue(5)：719-726,8.DOI:10.20079/j.issn.1001-893x.240415001
参数稀疏的复杂交通场景图像车辆检测
Parameter Sparse Vehicle Detection for Complex Traffic Scene Images
摘要
Abstract
Although the application of deep learning-based object detection in traffic scenes has made some progress,the game of multi-object accuracy and speed in complex traffic scenes is still a challenge.Most of methods for improving accuracy are parameter-intensive,which greatly increase parameters in the model.To address this challenge,a sparse parameter model based on YOLOv8 is proposed to achieve improved model recall and detection precision while reducing the number of parameters.Firstly,simple attention mechanism(SimAM)is used to build a stronger backbone network to extract features.Secondly,the lightweight content-aware reassembly of features(L-CARAFE)is proposed to replace the up-sampling operation in a larger sensing field to aggregate contextual information.Finally,the detection accuracy of the model is improved while the number of parameters is reduced through the multiple decoupling heads with sparse parameters.Considering the complexity of traffic scenes,not only the validity of the model is verified by the KITTI dataset,but also the generalisation of the model is verified by the COCO dataset.The model can significantly improve the recall and mean average precision(mAP)on both publicly available datasets,among which,nano improves the recall and mAP by 3.1％and 0.9％on the KITTI dataset with a parameter count of 2.95,and the small model achieves an mAP@0.5 of 60.6％on the COCO dataset.关键词
交通场景/目标检测/参数稀疏化/注意力机制Key words
traffic scenes/object detection/parameter sparse/attention mechanism分类
电子信息工程引用本文复制引用
韩雪娟,瞿中..参数稀疏的复杂交通场景图像车辆检测[J].电讯技术,2025,65(5):719-726,8.基金项目
国家自然科学基金资助项目(62176034) （62176034）
