重庆理工大学学报2026，Vol.40Issue(13)：31-37,7.DOI:10.3969/j.issn.1674-8425(z).2026.07.004
改进YOLOv11n的车辆目标检测算法
Research on YOLO-CCLW:an improved object detection algorithm for vehicles
摘要
Abstract
Currently,vehicle detection algorithms fail to well balance between accuracy and the number of parameters.To address the issue,this paper proposes an improved object detection algorithm YOLO-CCLW based on YOLOv11n.First,ConvFormer was integrated with convolutional gated linear units to design the C3k2_ConvFormer_CGLU module,significantly enhancing the network's global feature extraction capabilities.Then,the original detection head was replaced by a shared convolutional detection head,further reducing computational complexity and parameter count.Finally,the CIoU loss function was replaced with Wise-MPDIoU to enhance the model's localization and detection capabilities.Experiments were conducted on the KITTI dataset to verify the performance of the YOLO-CCLW algorithm.Results show YOLO-CCLW improves accuracy by 1.2％,recall rate by 4.6％,and mAP@0.5 by 3.0％compared to that of the traditional YOLOv11n.Meanwhile,the model's parameter count is down to 2.0M and the computation to 5.3G.While achieving superior detection accuracy,the improved algorithm requires fewer parameters.关键词
智能驾驶/目标检测/损失函数/特征提取/共享卷积Key words
intelligent driving/object detection/loss function/feature extraction/shared convolution分类
信息技术与安全科学引用本文复制引用
薛博文,郝亮..改进YOLOv11n的车辆目标检测算法[J].重庆理工大学学报,2026,40(13):31-37,7.基金项目
国家自然科学基金重点研发计划项目(U24A20283) （U24A20283）
辽宁省科技厅计划联合计划(技术攻关计划项目)(2024JH2/102600150) （技术攻关计划项目）
辽宁省科技厅成果转化类揭榜挂帅项目(2023JH1/11100003) （2023JH1/11100003）
