计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (11A): 241200131-8.doi: 10.11896/jsjkx.241200131

• 计算机图形学&多媒体 • 上一篇    下一篇

基于改进YOLOv8的城市交通视域下的目标识别算法

陈俊杰, 赵红, 罗勇, 丁晓云   

  1. 青岛大学机电工程学院 山东 青岛 266071
  • 出版日期:2025-11-15 发布日期:2025-11-10
  • 通讯作者: 赵红(qdlizh@163.com)
  • 作者简介:chenjunjie2@qdu.edu.cn
  • 基金资助:
    青岛市科技惠民示范专项(24-1-8-cspz-18-nsh)

Target Recognition Algorithm in Urban Traffic Field of View Based on Improved YOLOv8

CHEN Junjie, ZHAO Hong, LUO Yong, DING Xiaoyun   

  1. College of Mechanical and Electrical Engineering,Qingdao University,Qingdao,Shangdong 266071,China
  • Online:2025-11-15 Published:2025-11-10
  • Supported by:
    Qingdao Science and Technology Benefiting the People Demonstration Project(24-1-8-cspz-18-nsh).

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摘要： 为减少目标检测算法在城市环境下的误检和漏检问题,以YOLOv8目标检测算法模型为基础,引入小目标检测层,使得网络能够更好地捕捉和识别视野内的小尺寸物体,进而提高对检测目标的关注度;融合新型遥感目标检测模型来重构C2f模块,以增强其对丰富梯度流信息的感知能力,并增加其动态调节感受野的能力;通过采用拓扑优化思想来优化CBAM注意力机制,提出了GSAM注意力机制,并将其嵌入到网络的适当位置,以提高对语义信息的利用;改善漏检情况,通过对比多种IOU的性能,选择效果最优的EIOU,来加速算法的收敛速度,提高回归精度。在Cityscapes公开数据集上进行了测试和消融实验,实验结果表明改进后的算法相较于基准算法,在精确率、召回率、平均精度值方面分别提升了2.5个百分点、5.8个百分点、6.1个百分点,可以有效地提升城市交通视域下车辆的目标检测精度,为道路视频监控等提供保证。

关键词: 城市交通, 目标检测, YOLOv8, 注意力机制, IOU

Abstract: To reduce the issues of false detection and missed detection in target detection algorithms within urban environments,the YOLOv8 target detection model is used as the foundation,and a small-object detection layer is introduced to enable the network to better capture and recognize small-sized objects in the field of view,thereby improving its focus on target recognition.A novel remote sensing target detection model is integrated to reconstruct the C2f module,enhancing its perception of rich gradient flow information and its ability to dynamically adjust the receptive field.By applying topological optimization concepts to improve the CBAM attention mechanism,the GSAM attention mechanism is proposed and embedded at appropriate positions in the network to enhance the utilization of semantic information.To address the problem of missed detections,the performance of multiple IoU methods is compared,and the optimal EIoU is selected to accelerate the convergence speed of the algorithm and improve regression accuracy.Testing and ablation experiments conducted on the Cityscapes public dataset show that,compared to the baseline algorithm,the improved algorithm achieves increases of 2.5,5.8,and 6.1 percentage points in precision,recall,and mean average precision(mAP),respectively.These results effectively enhance the accuracy of vehicle target detection in urban traffic scenarios,providing reliable support for applications such as road video surveillance.

Key words: Urban traffic, Target recognition, YOLOv8, Attention mechanism, IOU

中图分类号: 

  • U495
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