[1]周恩泽,黄道春,王　磊,等.基于改进YOLOv8s的输电线路山火检测[J].郑州大学学报(工学版),2025,46(05):114-121.[doi:10.13705/j.issn.1671-6833.2025.05.017]
　ZHOU Enze,HUANG Daochun,WANG Lei,et al.Wildfire Detection for Transmission Corridor Based on Improved YOLOv8s[J].Journal of Zhengzhou University (Engineering Science),2025,46(05):114-121.[doi:10.13705/j.issn.1671-6833.2025.05.017]
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基于改进YOLOv8s的输电线路山火检测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年05期
页码:
114-121
栏目:
出版日期:
2025-08-10

文章信息/Info

Title:
Wildfire Detection for Transmission Corridor Based on Improved YOLOv8s
文章编号:
1671-6833(2025)05-0114-08
作者:
周恩泽1;  黄道春2;  王　磊1;  彭添浩2;  刘淑琴1;  汪　皓1;  陈　超1
1.广东电网有限责任公司电力科学研究院,广东 广州 510080;2.武汉大学 电气与自动化学院,湖北 武汉 430072
Author(s):
ZHOU Enze1;  HUANG Daochun2;  WANG Lei1;  PENG Tianhao2;  LIU Shuqin1;  WANG Hao1;  CHEN Chao1
1.Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China; 2.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
关键词:
输电线路;  山火烟雾;  目标检测;  YOLOv8s;  ODConv;  DyHead;  WIoU
Keywords:
transmission corridor;  wildfire smoke;  target detection;  YOLOv8s;  ODConv;  DyHead;  WIoU
分类号:
TP391.4 TM75
DOI:
10.13705/j.issn.1671-6833.2025.05.017
文献标志码:
A
摘要:
针对输电线路走廊复杂背景场景下传统预警方法山火检测效果差、速度慢,图像识别误检、漏检率较高等问题,提出了一种基于改进YOLOv8s的输电线路山火检测方法。首先,通过网络收集并对现有数据集筛选,得到以野外荒地为背景的山火图像数据集,更加贴合目标背景。其次,引入ODConv模块,对基线模型的Backbone和Neck部分使用C2f_OD模块替换原C2f模块进行特征提取,提升模型对火焰烟雾的检测性能;再更换Head部分为DyHead模块,融合尺度、空间和任务3种注意力感知模块,进一步提高检测精度;并使用WIoU损失函数,将检测框回归聚焦于普通质量的预测框,提升模型对复杂背景的泛化性能。最后,设计了3组消融实验和1组对比实验。实验结果表明:所提算法与原YOLOv8s模型相比,在自建山火数据集上mAP@0.5提高了5.6百分点,P提升了4.51百分点,R提升了5.41百分点,帧率为34.9帧/s,满足输电线路山火精准检测的要求。
Abstract:
In the complex background of transmission line corridors, traditional early warning methods for detecting wildfires showed poor performance, slow detection speeds, and high rates of false positives and missed detections in image recognition. In this study a wildfire detection method was introduced for transmission corridor based on an improved YOLOv8s model. Firstly, through network collection and screening of existing datasets, a wildfire image dataset featuring wilderness backgrounds was obtained, providing a more suitable match for the target background. Secondly, the ODConv module was introduced, replacing the original C2f module with the C2f_OD module for feature extraction in the Backbone and Neck sections of the baseline model, thereby enhancing the model′s detection performance for flames and smoke. Secondly, the Head section was replaced with the DyHead module, integrating three attention mechanisms scale, spatial, and task to further improve detection accuracy. The WIoU loss function was employed to focus detection frame regression on prediction boxes of ordinary quality, enhancing the model′s generalization performance in complex backgrounds. Finally, three ablation experiments and one comparative experiment were designed. The results demonstrated that, compared to the original YOLOv8s model, the proposed algorithm achieved 5.6% increase in mAP@0.5, 4.51% increase in P, 5.41% increase in R, and a detection speed of 34.9 frames per second, meeting the requirements for accurate wildfire detection along transmission corridor.

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更新日期/Last Update: 2025-09-19