Open AccessDetection of Abnormal Hot Spots Infrared Images of Power Equipment Based on YOLOv4
Author(s) -
Xiao Qi,
Lei Shi,
Lei Li,
Fangsen Chai,
Dongxu Han,
Chong Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2005/1/012074
Subject(s) - computer science , artificial intelligence , object detection , fault (geology) , fault detection and isolation , set (abstract data type) , grid , power (physics) , computer vision , pattern recognition (psychology) , object (grammar) , image (mathematics) , stability (learning theory) , mathematics , machine learning , physics , geometry , quantum mechanics , seismology , actuator , programming language , geology
The stable operation of power equipment is very important to ensure the stability of power grid. In this paper, an object detection model is designed for detection of the abnormal hot spots of electrical equipment. In order to improve the performance of the object detection model, data augmentation of image is applied to extending the training set. The test results show that the model based on YOLOv4 can identify and locate the abnormal heating fault point in the infrared image with high accuracy. The precision and recall rate of the object detection model on test set can reach 88.51% and 91.66% respectively.