基于YOLOv8和SVDD重识别的交通信号灯检测方法  

Traffic Light Detection Method Based on YOLOv8 and Re-identification Using SVDD

作　　者：余杨 何刘 YU Yang;HE Liu(Sichuan Provincial Key Laboratory of Vehicle Measurement,Control and Safety,Xihua University,Chengdu 610039,China)

机构地区：[1]西华大学汽车测控与安全四川省重点实验室,四川成都610039

出　　处：《汽车实用技术》2024年第23期49-54,共6页Automobile Applied Technology

摘　　要：为提升交通信号灯的检测精度和识别准确率,文章提出基于YOLOv8的SVDD-YOLOv8目标检测方法,该方法通过整合全局注意力机制(GAM)和支持向量数据描述(SVDD)分类模块,强化特征捕捉并二次确认目标,同时引入EIoU损失函数提高定位精度。对SVDD别出的异常区域进行再训练,提升模型性能。实验显示,该方法较YOLOv8在检测精度和mAP@0.5上分别提升7.75%和8.99%,证明了该方法在提高交通信号灯检测精度和抗干扰能力上的有效性。To improve the detection accuracy and recognition precision of traffic lights,this paper proposes an SVDD-YOLOv8 target detection method based on YOLOv8.This method enhances feature capture and reconfirms targets by integrating the global attention mechanism(GAM)and support vector data description(SVDD)classification module.Meanwhile,the embedding intersection over union(EIoU)loss function is introduced to improve positioning accuracy.The abnormal areas identified by SVDD are retrained to enhance the model's performance.Experiments show that this method improves detection accuracy and mAP@0.5 by 7.75%and 8.99%,respectively,compared to YOLOv8,demonstrating the effectiveness of this approach in improving traffic light detection accuracy and anti-interference ability.

关 键 词：交通信号灯 目标检测 SVDD YOLOv8 EIoU 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] U495[自动化与计算机技术—计算机科学与技术]