森林工程2025，Vol.41Issue(5)：1013-1024,12.DOI:10.7525/j.issn.1006-8023.2025.05.014
基于改进YOLOv8n的林草火灾检测算法
Forest and Grass Fire Detection Algorithm Based on Improved YOLOv8n
摘要
Abstract
In forest and grassland fire scenarios,the diversity of open flame forms and the complexity of the environment may lead to false or missed detection.Therefore,an improved YOLOv8n fire detection algorithm(YOLOv8n-CSA)is proposed for forest and grassland fires.CSA(channel-spatial attention)is the channel spatial attention module,and a group shuffle convolution(GSConv)module is introduced to replace the third layer standard convolution module(Conv)in the original YOLOv8n,reducing model computation and improving feature extraction ability.And introducing the Slim Neck structure in the head further reduces the computational complexity of the model.Simultaneously design a channel spatial attention module(CSA)integrated into the Backbone section to enhance the expressive power of the input feature map.This module combines channel attention,channel shuffle,and spatial attention mechanisms to cap-ture global dependencies within feature maps.Based on a forest and grassland fire dataset,and without utilizing pre-trained models,the proposed fire detection network achieves a 3.7％increase in precision,a 1.51％improvement in re-call,a 3.24％enhancement in mAP50,and a 5.62％reduction in GFLOPs compared to the baseline YOLOv8n model.Experimental results demonstrate that the proposed algorithm not only reduces computational cost but also enhances the detection performance of fire-related features.关键词
火灾检测/YOLOv8/通道空间注意力/Slim-Neck结构/分组混洗卷积模块GSConvKey words
Fire detection/YOLOv8/channel-spatial attention/Slim-Neck stucture/group shuffle convolution GSConv分类
农业科技引用本文复制引用
赵佳硕,马晓春,刘舰泽..基于改进YOLOv8n的林草火灾检测算法[J].森林工程,2025,41(5):1013-1024,12.基金项目
中央高校基本科研业务费专项资金项目(2572017DB01). （2572017DB01）
