Wildfire Detection for Transmission Corridor Based on Improved YOLOv8s

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Wildfire Detection for Transmission Corridor Based on Improved YOLOv8s

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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 05 Page number: 114-121 Column: Public date: 2025-08-10

Title:: Wildfire Detection for Transmission Corridor Based on Improved YOLOv8s

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

Keywords:: transmission corridor; wildfire smoke; target detection; YOLOv8s; ODConv; DyHead; WIoU

CLC:: TP391.4 TM75

DOI:: 10.13705/j.issn.1671-6833.2025.05.017

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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Last Update: 2025-09-19