[1]郭 庆,陈 川,季强东,等.基于改进 YOLOv11 的复杂环境下火焰目标检测[J].机械与电子,2025,(11):33-39.
　GUO Qing,CHEN Chuan,JI Qiangdong,et al.Flame Target Detection in Complex Environments Based on Improved YOLOv11[J].Machinery & Electronics,2025,(11):33-39.
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基于改进 YOLOv11 的复杂环境下火焰目标检测()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年11期
页码:
33-39
栏目:
自动控制与检测
出版日期:
2025-11-24

文章信息/Info

Title:
Flame Target Detection in Complex Environments Based on Improved YOLOv11
文章编号:
1001-2257 ( 2025 ) 11-0033-07
作者:
郭 庆 1 ; 陈 川 1 ; 季强东 1 ; 权晓柯 1 ; 易灿灿 2
1. 格林美(武汉)城市矿山产业集团有限公司,湖北 武汉 431411 ;
2. 武汉科技大学机械工程学院,湖北 武汉 430081
Author(s):
GUO Qing1 ;  CHEN Chuan1 ;  JI Qiangdong1 ;  QUAN Xiaoke1 ;  YI Cancan2
( 1.Greenway ( Wuhan ) Urban Mining Industry Group Co. , Ltd. , Wuhan 431411 , China ;
2.School of Mechanical Engineering , Wuhan University of Science and Technology , Wuhan 430081 , China )
关键词:
深度学习; 目标检测;  YOLO 模型; 多尺度特征; 注意力机制
Keywords:
deep learning ;  target detection ;  YOLO model ;  multi-scale features ;  attention mechanism
分类号:
TP183 ;TP391.4
文献标志码:
A
摘要:
针对复杂环境下火焰目标检测的实时性与准确性需求,提出一种改进的 YOLOv11 火焰检测模型。在模型优化改进方面,通过引入 C3k2 _ Ghost 模块降低计算复杂度,设计特征提炼模块( FRM )增强多尺度特征表达能力,采用 SEAM 注意力机制优化遮挡场景下的检测性能,并增加 160×160 小目标检测层以提升细节捕获能力。实验基于 4 653 张火焰图像数据集,改进模型的 mAP 达 86.1% ,召回率为 84.3% ,检测速度达 64.1 帧/ s,较 YOLOv11 、 YOLOv8 等主流模型在精度与效率上均显著提升。结果表明,该模型有效平衡了轻量化设计与特征表达能力,为复杂工业场景下的实时火灾预警提供了高效解决方案。
Abstract:
To address the real-time and accuracy demands of flame target detection in complex environments , this study proposes an enhanced YOLOv11 flame detection model.In terms of model optimization , the C3k2 _ Ghost module is introduced to reduce computational complexity , and a Feature Refinement Module ( FRM ) is designed to enhance multi-scale feature representation.Furthermore , the SEAM attention mechanism is employed to optimize detection performance in occlusion scenarios , and an additional detection layer at 160×160 is incorporated to improve the ability to capture fine details.Experimental results based on a dataset of 4 653 flame images demonstrate that the improved model achieves a mean Average Precision ( mAP ) of 86.1% , a recall rate of 84.3% , and a detection speed of 64.1 FPS , significantly outperforming mainstream models such as YOLOv11 and YOLOv8 in both accuracy and efficiency.The results indicate that the proposed model effectively balances lightweight design and feature representation capabilities , providing an efficient solution for real-time fire hazard warning in complex industrial scenarios.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2025-05-30
基金项目:国家自然科学基金资助项目( 51805382 );湖北省应急管理厅安全生产专项资金科技项目( GEM-CK-JS-2023033101 )
作者简介:郭 庆 ( 1994- ),男,河南唐河人,工学博士,工程师,研究方向为报废汽车与退役动力电池循环利用技术;易灿灿 ( 1989- ),男,湖北荆州人,博士研究生,副教授,研究方向为设备状态监测,通信作者, E-mail : 2264993646@qq.com 。
更新日期/Last Update: 2025-12-12