基于改进YOLOv5的树莓派火焰识别系统

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。

Raspberry Pi flame recognition system based on improved YOLOv5

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.