汽车工程学报2026，Vol.16Issue(2)：227-235,9.DOI:10.3969/j.issn.2095‒1469.2026.02.06
基于注意力机制优化的YOLOv7目标检测算法
An Optimized YOLOv7 Object Detection Algorithm Based on Attention Mechanisms
摘要
Abstract
To ensure that an autonomous-driving system can accurately and swiftly identify road targets,this paper integrates SE,CBAM,and ECA attention modules into the YOLOv7 backbone network and designs an optimized network capable of feature recalibration.The network model is trained on the publicly available KITTI dataset,and a data processing platform is established.The performance of the network model and its detection of Car,Pedestrian,and Cyclist classes are compared before and after optimization with the SE attention module.Comparative experimental results demonstrate that the optimization algorithm incorporating the SE attention module achieves a better balance between computational efficiency and accuracy.Ablation studies further confirm that the SE module provides the primary performance gain,and the fused network balances detection accuracy with real-time performance.After optimization,the mean Average Precision(mAP)of the network increases by 1.16%,the FPS drop remains within 7%,and detection accuracies for Car,Pedestrian,and Cyclist improve by 3.17%,3.88%,and 2.77%respectively,effectively reducing missed and false detection rates.These results validate the effectiveness and practical value of the attention-optimized YOLOv7 algorithm for object detection in complex environments,providing robust technical support for the safety and reliable perception of autonomous driving systems.关键词
道路目标信息/YOLOv7目标检测算法/注意力机制/KITTI数据集Key words
road target information/YOLOv7 object detection algorithm/attention mechanism/KITTI data set分类
交通工程引用本文复制引用
张洪梅,张云飞,杨良义,李斌,张强..基于注意力机制优化的YOLOv7目标检测算法[J].汽车工程学报,2026,16(2):227-235,9.基金项目
重庆市人力资源和社会保障局重庆英才计划(CQYC20220207209) （CQYC20220207209）
