Abstract: The demand for the reliability of power grid systems is gradually increasing with the development of the power industry. And it is necessary to promptly identify and eliminate the hidden dangers. To meet the needs of online monitoring and the early warning of electrical equipment, an intelligent detection system based on deep learning and infrared image processing technology is proposed in this study. Firstly, the infrared image is preprocessed for noise reduction. Then, an improved SSD (Single Shot MultiBox Detector) network is used to optimize the infrared image detection method. Based on this, an intelligent detection system for electrical equipment is designed. The results show that the mAP value of the improved SSD network after 1200 iterations is about 92.58%, and its area under the Precision Recall (PR) curve is higher than other algorithms. The simulation analysis results of the detection system show that the improved method detects a fault degree of 57.85%, which is closer to the 59.74% in the real situation. The experimental results indicate that the newly established intelligent detection system for electrical equipment can effectively detect its abnormal situations.

Mingxu Lu and Yuan Xie, “Intelligent Detection System for Electrical Equipment based on Deep Learning and Infrared Image Processing Technology” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01408124

@article{Lu2023,
title = {Intelligent Detection System for Electrical Equipment based on Deep Learning and Infrared Image Processing Technology},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01408124},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01408124},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Mingxu Lu and Yuan Xie}
}