Anomaly Detection of Substation Equipment and Operational Behavior Based on the Improved YOLOv11n Model
WEI Yingao
XU Jian
LI Yinghao
HU Haote
Abstract：To address the current issues of low detection efficiency and high manual risks in detecting abnormal equipment and operational behavior in substations due to manual inspections,an improved you only look once version 11 nano(YOLOv11n)model was proposed.Firstly,a convolutional three-scale kernel-adaptive dual-path self-attention(C3k2-SA)module was designed,which was used to optimize the network structure and enhance the global feature extraction capability in the small feature map fusion section.Subsequently,a feature enhancement(FEN)module based on the attention mechanism was added to the final layer of the backbone network.This module dynamically adjusted the feature weights of different regions,enabling adaptive feature enhancement and alleviating the gradient vanishing problem in deep networks.Finally,the concatenate(Concat)module was optimized by adjusting the channel numbers through convolution layers,with pooling and sigmoid activation function applied for fine-grained feature processing.The model′s adaptability to different types of features and the feature fusion effect were strengthened and irrelevant or redundant features were suppressed to prevent overfitting.The results showed the precision,recall,and mean average precision of the improved YOLOv11n model increased by 1.7,6.6 and 3.6 percentage point respectively,compared to the original YOLOv11n model.The improved YOLOv11n model could be used to enhance the accuracy of abnormal state detection in substations and provide valuable insights for abnormal detection tasks in intelligent substations.
Keywords：object detectionYOLOv11nactivation functionself-attention mechanismC3k2adaptive average pooling
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( 41-46 )