沈阳航空航天大学学报2026，Vol.43Issue(2)：70-76,7.DOI:10.3969/j.issn.2095-1248.2026.02.010
基于YOLOv8n-MAAI的火灾图像检测
Fire image detection based on YOLOv8n-MAAI
摘要
Abstract
In order to improve the accuracy and efficiency of fire monitoring in complex enviroments,an improved YOLOv8n model named YOLOv8n-MAAI for fire detection was proposed.Firstly,by incorporating a multi-head self-attention(MHSA)mechanism into the backbone network,the model's ability to handle complex scenarios was enhanced,improving detection accuracy.Furthermore,the ADown module from YOLOv9 was integrated into the YOLOv8 architecture,reducing computational complexity through a lightweight design.The adaptive spatial feature fusion(ASFF)mechanism was introduced to form a new Detect_ASFF detection header,filtering conflicting information and improving feature scale invariance.Finally,the Inner-MPDIoU loss function was employed to optimize the training process,enhancing regression accuracy and accelerating model convergence.Experimental results demonstrate that YOLOv8n-MAAI achieves a detection precision of 92.4%,a recall rate of 93.6%,and a mean average precision(mAP)of 94.3%,outperforming both the original YOLOv8n and other popular detection models such as YOLOv5s and YOLOv3-tiny.These improvements provide a more accurate and reliable solution for fire detection,supporting advancements in fire prevention and control technology.关键词
火灾图像检测/目标检测/YOLOv8/多头自注意力机制/损失函数Key words
fire image detection/target detection/YOLOv8/multi-head self-attention mechanism/loss function分类
资源环境引用本文复制引用
曲娜,时尚,张晗,魏文龙..基于YOLOv8n-MAAI的火灾图像检测[J].沈阳航空航天大学学报,2026,43(2):70-76,7.基金项目
国家自然科学基金(项目编号:61901283) （项目编号:61901283）
辽宁省自然科学基金(项目编号:2023-MS-241). （项目编号:2023-MS-241）
