广东电力2025，Vol.38Issue(8)：41-52,12.DOI:10.3969/j.issn.1007-290X.2025.08.005
基于改进YOLOv10n的变电设备红外图像检测
Infrared Image Detection of Substation Equipment Based on Improved YOLOv10n
摘要
Abstract
Infrared imaging technology plays a crucial role in substation equipment monitoring by identifying potential faults through temperature anomalies.However,infrared image detection on substation equipment presents several challenges,such as small target size,low contrast,and high real-time processing demands.Existing target detection methods are difficult to balance accuracy and efficiency.To address these issues,this study proposes a lightweight detection model based on an improved YOLOv10n to enhance the recognition performance of substation equipment in infrared images.Firstly,the C2f module of the backbone network is optimized by introducing a global-to-local spatial aggregation module to improve focus on small targets while reducing computational load.Secondly,a frequency-aware feature fusion module and frequency-domain-aware path aggregation network are introduced into the neck network to effectively solve the problems of target boundary blurring and shifting,while further reducing the model size.Finally,the SIoU loss function is used to replace the traditional CIoU loss function,improving target location accuracy and accelerating model training.Experiments conducted on a substation infrared image dataset show that the improved model significantly outperforms the baseline YOLOv10n model in detection performance.Specifically,the model achieves a mean average precision of 97.6%,an improvement of 3.4%over the original model,while significantly reducing computational complexity and the number of parameters,making it suitable for deployment in resource-constrained environments.This has important implications for ensuring the safety and stability of power grid operations.关键词
变电设备/目标检测/红外图像/YOLOv10n/轻量化模型Key words
substation equipment/target detection/infrared image/YOLOv10n/lightweight model分类
信息技术与安全科学引用本文复制引用
潘国清,洪永学,邵天赐,杨强..基于改进YOLOv10n的变电设备红外图像检测[J].广东电力,2025,38(8):41-52,12.基金项目
国家自然科学基金(52177119) （52177119）
