基于改进YOLOv5的树莓派火焰识别系统
Raspberry Pi flame recognition system based on improved YOLOv5
邓力 1谢爽爽 1朱博 1吴丹丹 1刘全义1
作者信息
- 1. 中国民用航空飞行学院 民航安全工程学院,四川 广汉 618307
- 折叠
摘要
火灾会对人员与财产安全造成巨大危害,如何迅速、准确地检测火焰出现具有重要意义.为实现高大空间条件下火焰的准确识别,设计了一种具有二自由度、可全方位检测环境情况的红外摄像头,并结合深度学习对目标检测算法YOLOv5进行改进;利用K-Means聚类算法匹配出9个聚类中心宽高维度替换原网络anchor参数;考虑了目标框相对比例,对损失函数进行优化,并用于树莓派上实现火焰识别.测试结果表明:改进的YOLOv5算法在树莓派上单张检测耗时 2.9 s,较无改进YOLOv5 算法减少 78%;系统查准率为 100%,识别目标框置信度均在 0.9 以上,能够快速准确识别出火焰.
Abstract
Fire disaster can cause great harm to the safety of people and property,and how to detect flame intelligently and efficiently is of great significance.In order to achieve accurate flame recognition under high space conditions,an infrared camera with two degrees of freedom that can detect environmental conditions in all directions is designed,and the target detection algorithm YOLOv5 is improved combined with deep learning.The K-Means clustering algorithm is employed to obtain nine width and height dimensions of clustering center by matching and replace the original network anchor parameters.Considering the relative proportion of the target frame,the loss function is optimized and applied to the Raspberry Pi to achieve flame recognition.The test results show that it takes 2.9 s for the improved YOLOv5 algorithm to detect a single sheet on the Raspberry Pi,which is less than that for the original YOLOv5 algorithm by 78%.The accuracy of the system is 100%,and the confidence of identifying the target frame is above 0.9.The proposed system can identify the flame fast and accurately.
关键词
深度学习/YOLOv5算法/树莓派/火焰识别Key words
deep learning/YOLOv5/Raspberry Pi/flame recognition引用本文复制引用
基金项目
国家自然科学基金资助项目(U2033206)
国家自然科学基金资助项目(U1933105)
四川省重点实验室重点资助项目(MZ2022JB01)
航空科学基金资助项目(20200046117001)
德阳市科技局重点研发资助项目(2021SZ001)
中国民用航空飞行学院基金资助项目(J2020-120)
出版年
2024
国家科技期刊平台
NSTL
万方数据