计量学报2024，Vol.45Issue(9)：1314-1323,10.DOI:10.3969/j.issn.1000-1158.2024.09.08
一种改进YOLOv5s的森林火灾烟雾检测算法
An Improved YOLOv5s Forest Fire Smoke Detection Algorithm
摘要
Abstract
A forest fire smoke detection algorithm based on improved YOLOv5s is proposed.A fire and smoke dataset containing 16573 images is constructed to solve the problem of insufficient training data sets and improve the generalization ability of the training model.A lightweight GC-C3 module is designed to replace the original C3 module and reduce the number of model parameters and calculation.The weighted bidirectional feature pyramid network is integrated into the Neck structure to enhance the detection ability of the network for small and medium targets.The network space pyramid pool structure is modified,SPPF is replaced by SimSPPF structure,and the computing efficiency and detection accuracy of the network are improved.The bounding box regression loss function CIOU is replaced by Focal-EIOU to accelerate the convergence of the model and solve the problem of mismatch between positive and negative samples.The experimental results show that the average detection accuracy of the improved network is increased by 2.3％,the number of model parameters is decreased by 46.7％,and the calculation amount of the model is decreased by 47.5％.关键词
机器视觉/火灾烟雾检测/深度学习/YOLOv5s/轻量化/小目标检测/Focal-EIOUKey words
machine vision/fire smoke detection/deep learning/YOLOv5s/light weight/small target detection/Focal-EIOU分类
通用工业技术引用本文复制引用
张立国,张琦,金梅,袁煜淋,王泓沣..一种改进YOLOv5s的森林火灾烟雾检测算法[J].计量学报,2024,45(9):1314-1323,10.基金项目
河北省中央引导地方专项(199477141G) （199477141G）
