一种改进的YOLOv5视频火焰实时检测算法
AN IMPROVED YOLOV5 VIDEO REAL-TIME FLAME DETECTION ALGORITHM
张智 1冯双文1
作者信息
- 1. 武汉科技大学计算机科学与技术学院 湖北 武汉 430065;智能信息处理与实时工业系统湖北省重点实验室 湖北 武汉 430065;武汉科技大学大数据科学与工程研究院 湖北 武汉 430065
- 折叠
摘要
针对在室内外的火灾预防,目前许多算法对于小目标的火焰检测在精度方面有所欠缺,且不能实时检测,故提出一种改进的YOLOv5 算法.该算法加宽head层数并引入selayer层,加快了分类检测的收敛,得到更丰富的采样信息.改进后的算法精度大大提高,经过视频流的优化,火焰能被实时地检测出来.实验数据集上的结果表明,改进的YOLOv5 模型精确率达到了80.4%,召回率达到了91.3%,检测速度达到每秒44 帧.
Abstract
For indoor and outdoor fire prevention,many present algorithms for small target fire detection are lack of accuracy,and can not detect in real time,so an improved YOLOv5 algorithm is proposed.The algorithm widened the number of head layers and introduced selayer layer to accelerate the convergence of classification detection and get more abundant sampling information.The accuracy of the improved algorithm was greatly improved.After the optimization of video stream,the flame could be detected in real time.The experimental results show that the accuracy rate of the improved YOLOv5 model reaches 80.4%,the recall rate reaches 91.3%,and the detection speed reaches 44 frames per second.
关键词
卷积神经网络/小目标检测/视频流优化Key words
Convolutional neural network/Small target detection/Video stream optimization引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据