计量学报2025，Vol.46Issue(5)：659-666,8.DOI:10.3969/j.issn.1000-1158.2025.05.06
面向交通执法中车辆使用远光灯检测的YOLOv10-ECC算法研究
Research on YOLOv10-ECC Algorithm for Vehicle High-beam Detection for Traffic Enforcement Scenarios
摘要
Abstract
To rapid and accurate detection of vehicles using high-beam headlights can assist traffic management departments in efficient law enforcement.An end-to-end real-time YOLOv10-ECC detection algorithm is proposed.Firstly,the PSA_EMA module is designed in the backbone of YOLOv10,which uses the efficient multi-scale attention module(EMA)to capture more abundant feature information while maintaining the parameter efficiency.Secondly,the convolutional feature recombination module(CCM)is designed in the neck,which realizes the up-sampling feature enhancement and constructs the more subtle feature map.The experimental results on the self-collected high-beam vehicles dataset show that the mean average accuracy mAP@0.5 of YOLOv10-ECC reaches 93.2%,the number of parameters is 2.86×106,the detection time of a single photo is 2.2 ms,and the computational complexity is 9.0 GFLOP/s,which can detect high-beam vehicles in real time and with high accuracy.关键词
机械视觉测量/远光灯检测/YOLOv10-ECC/EMA模块/CCM模块/上采样算子/端到端Key words
mechanical vision measurement/high-beam detection/YOLOv10-ECC/EMA module/CCM module/CARAFE/end-to-end引用本文复制引用
张立立,张珂,杨康,魏薇,李晶,谭洪鑫..面向交通执法中车辆使用远光灯检测的YOLOv10-ECC算法研究[J].计量学报,2025,46(5):659-666,8.基金项目
宁夏自然科学基金(2022AAC03757,2023AAC03889) （2022AAC03757,2023AAC03889）
北京市数字教育研究课题(BDEC2022619048) （BDEC2022619048）
北京市教育委员会科研计划项目(KM202410017006) （KM202410017006）
北京市高等教育学会课题(MS2022144) （MS2022144）
