基于YOLOv3-tiny的二轮车头盔检测
HELMET DETECTION OF TWO WHEELED VEHICLE BASED ON YOLOV3-TINY
杨国亮 1李世聪 1邹俊峰 1龚家仁1
作者信息
- 1. 江西理工大学电气工程与自动化学院 江西赣州 341000
- 折叠
摘要
针对二轮车驾乘人员头盔佩戴问题,提出一种基于YOLOv3-tiny的轻量化头盔检测模型.将原始模型主干网络进行轻量化处理,减少检测模型的参数量,在网络中添加U型特征二次融合模块,引入关于边框距离的DIoU损失函数,用于提高检测模型的特征提取能力和识别精度.在测试集上的实验表明,改进后的模型相比原YOLOv3-tiny模型表现出更高的查全率和mAP及F1指标,且在保持较小参数量的同时,具有优于深度网络YOLOv3的检测性能.
Abstract
Aimed at the helmet wearing problem of two-wheeled vehicle drivers and passengers,a lightweighted helmet detection model based on YOLOv3-tiny is proposed.The original model backbone network was light-weighted to reduce the amount of parameters of the detection model,and a U-shaped feature secondary fusion module was added to the network.The DIoU loss function about the distance of bounding boxes was introduced to improve the feature extraction ability of the detection model and recognition accuracy.Experiments on the test set show that the improved model exhibits higher recall rate and mAP and F1-score than the original YOLOv3-tiny model,and it has better detection performance than the deep network YOLOv3 while maintaining a small amount of parameters.
关键词
头盔检测/轻量化网络/特征融合/边框损失函数Key words
Helmet detection/Lightweighted network/Feature fusion/Bounding box loss function引用本文复制引用
基金项目
国家自然科学基金(51365017)
江西省教育厅科技项目(GJJ190450)
出版年
2024
国家科技期刊平台
NSTL
万方数据