太赫兹科学与电子信息学报2024，Vol.22Issue(7)：776-780,5.DOI:10.11805/TKYDA2022156
基于改进YOLOv5的树莓派火焰识别系统
Raspberry Pi flame recognition system based on improved YOLOv5
摘要
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分类
信息技术与安全科学引用本文复制引用
邓力,谢爽爽,朱博,吴丹丹,刘全义..基于改进YOLOv5的树莓派火焰识别系统[J].太赫兹科学与电子信息学报,2024,22(7):776-780,5.基金项目
国家自然科学基金资助项目(U2033206 （）
U1933105) （）
四川省重点实验室重点资助项目(MZ2022JB01) （MZ2022JB01）
航空科学基金资助项目(20200046117001) （20200046117001）
德阳市科技局重点研发资助项目(2021SZ001) （2021SZ001）
中国民用航空飞行学院基金资助项目(J2020-120) （J2020-120）
