The Lightweight Power Equipment Overheating Fault Infrared Images Detection Algorithm Based on the Improved YOLOv11n
ZHOU Yunlei
DONG Xiaojie
LIU Sanjun
LIU Chengyi
Abstract：To address the issue of detecting overheating faults in power equipment due to the complex background in infrared images of substations,an improved you only look once version 11 nano(YOLOv11n)algorithm was proposed.Firstly,the original neck network was improved by adopting a lightweight cross-scale feature fusion module(CCFM)to achieve efficient integration of feature channel information and reduce the model′s parameter quantity.Secondly,a cross stage partial with three-convolution blocks of variable kernel size two-switchable atrous convolution(C3k2-SAConv)module was introduced to replace the C3k2 module across the entire network,which enhanced the model′s feature extraction capability.Finally,a cross stage partial with two convolutions and vision transformer of bi-level routing attention(C2BF)module was used to replace the cross stage partial with two convolutions and pointwise spatial attention(C2PSA)module,which improved the model′s accuracy in detecting targets in infrared images under complex environments.The results showed that compared to the original YOLOv11n algorithm,the improved YOLOv11n algorithm parameter quantity was reduced by 22.1%;precision,recall,and mean average precision reached 91.1%,85.5%,and 90.9%,respectively,with improvements of 3.0,2.6 and 2.8 percentage point;the detection speed reached 128.2 frames/s.The improved YOLOv11n quantity was able to effectively detect overheating faults of power equipment in infrared images,meeting the requirements for lightweight and real-time detection.
Keywords：infrared imageYOLOv11noverheating fault detectionCCFMC3k2-SAConvC2BF
Publication Date：2025-03-19
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 114-118,140 )