基于轻量化YOLOv8的安全帽检测
Safety Helmet Detection Based on Lightweight YOLOv8
张碧川 1刘卫东 1米浩 1景亚宁1
作者信息
- 1. 山西师范大学 物理与信息工程学院,山西 太原 030092
- 折叠
摘要
安全帽检测是一项具有重要应用价值的计算机视觉任务,涉及建筑工地、矿山、电力等多个领域的安全管理.然而,安全帽检测也面临着诸多挑战,如目标尺寸和长宽比的巨大变化、目标速度的快速变化、目标遮挡和背景干扰等.为了解决这些问题,提出了一种基于轻量化YOLOv8算法的安全帽检测方法,利用YOLOv8的高速和高精度特点,结合安全帽的特征,实现了对安全帽的有效检测.
Abstract
Helmet detection is a computer vision task with important application value,involving safety management in many fields such as construction sites,mines,and electric power.However,helmet detection also faces many challenges,such as large changes in target size and aspect ratio,rapid changes in target velocity,target occlusion,and background interference.In order to solve these problems,this paper proposes a safety helmet detection method based on YOLOv8,which uses the characteristics of high speed and high precision of YOLOv8 combined with the characteristics of safety helmets to achieve effective detection and identification of safety helmets.
关键词
轻量化/YOLOv8算法/python/目标检测/图像分割/计算机视觉Key words
lightweighting/YOLOv8 algorithm/python/object detection/image segmentation/computer vision引用本文复制引用
基金项目
山西师范大学2022年大学生创新创业训练计划项目(2022DCXM-53)
出版年
2024
NETL
NSTL
维普
万方数据