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\begin{document}

\title{Thermal Anomaly Area Intelligent Recognition Method Based on Infrared
Inspection Image of Electrical Equipment}



\author[1]{Laiqi Wang}%
\author[1]{Jianhua Ou}%
\author[1]{Jianguo Wang}%
\author[2]{Cong Hu}%
\author[1]{Yadong Fan}%
\author[1]{Fuhua Xie}%
\affil[1]{Wuhan University}%
\affil[2]{China Southern Power Grid Co Ltd}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
The thermal anomaly area of electrical equipment in the substation is
often hidden due to its small thermal area and multiple anomalies
overlaid. Accurately identify the thermal area is demanded on the
condition detection of electrical equipment, where the anomaly points of
electrical equipment in infrared images are generally small and of low
resolution. We propose an improved YOLOv4 algorithm for infrared image
anomaly area identification, which can detect the thermal generation
phenomenon of electrical equipment. We add a new target detection branch
to the shallow feature map of 104\selectlanguage{ngerman}×104, which can better extract small
target semantic information. The training process is enhanced with
cosine annealing and mosaic data enhancement. We establish a total of
719 infrared images of five types of thermal anomalies electrical
equipment to test our network. The accuracy of our model reach to as
high as 96.78\%, with a detection speed of 17 f/s and an AP@0.5 of
94.23\%. Compared with SSD, YOLOv4 and Faster RCNN, the algorithm in
this paper obtains the highest AP@0.5 with 94.23\%, which is the best
performance compared with the original YOLOv4 model in accuracy. The
model is robust to noise and luminance disturbances, and still provides
good recognition under disturbances.%
\end{abstract}\selectlanguage{ngerman}%



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