A Small-Sample Target Detection Method for Transmission Line Hill Fires Based on Meta-Learning YOLOv11

Yaoran Huo, Yang Zhang, Jian Xu, Xu Dai, Luocheng Shen, Conghong Liu and Xia Fang ()
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Yaoran Huo: Information & Communication Company, State Grid Sichuan Electric Power Company, Chengdu 610065, China
Yang Zhang: Information & Communication Company, State Grid Sichuan Electric Power Company, Chengdu 610065, China
Jian Xu: Information & Communication Company, State Grid Sichuan Electric Power Company, Chengdu 610065, China
Xu Dai: Information & Communication Company, State Grid Sichuan Electric Power Company, Chengdu 610065, China
Luocheng Shen: Information & Communication Company, State Grid Sichuan Electric Power Company, Chengdu 610065, China
Conghong Liu: Information & Communication Company, State Grid Sichuan Electric Power Company, Chengdu 610065, China
Xia Fang: School of Mechanical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2025, vol. 18, issue 6, 1-14

Abstract: China has a large number of transmission lines laid in the mountains and forests and other regions, and these transmission lines enable national strategic projects such as the west-east power transmission project. However, the occurrence of mountain fires in the corresponding areas will seriously affect these transmission projects. At the same time, these mountain fires yield fewer image samples and complex backgrounds. Based on this, this paper proposes a transmission line hill fire detection model with YOLOv11 as the basic framework, named meta-learning attention YOLO (MA-YOLO). Firstly, the feature extraction module in it is replaced with a meta-feature extraction module, and the scale of the detection head is adjusted to detect smaller-sized hill fire targets. After this, the re-weighting module learns class-specific re-weighting vectors from the support set samples and uses them to recalibrate the mapping of meta-features. To enhance the model’s ability to learn target hill fire features from complex backgrounds, adaptive feature fusion (AFF) is integrated into the feature extraction process of YOLOv11 to improve the model’s feature fusion capabilities, filter out useless information in the features, and reduce the interference of complex backgrounds in detection. The experimental results show that the accuracy of MA-YOLO is improved by 10.8% in few-shot scenarios. MA-YOLO misses fewer hill fire targets in different scenarios and is less likely to be affected by complex backgrounds.

Keywords: few-shot; meta-learning; adaptive feature fusion (AFF); spatial and channel reconstruction convolution; transmission lines; hill fire detection; convolutional neural network; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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