Automatic hot spot edge-detection method for photovoltaic aerial infrared image

Jing Yang; Mingyong Xin; Qihui Feng

doi:10.1117/12.3011341

8 November 2023 Automatic hot spot edge-detection method for photovoltaic aerial infrared image

Jing Yang, Mingyong Xin, Qihui Feng

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129231O (2023) https://doi.org/10.1117/12.3011341
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

A two-stage hotspot detection method for aerial infrared images is proposed to address the issues of high cost, low efficiency, and low accuracy in traditional photovoltaic power plant detection technology. This method achieves component level localization and fine classification diagnosis of hotspot defects in infrared images. The method proposed in this paper is to integrate deep learning algorithms and traditional image algorithms to better identify defects. Firstly, this paper uses the edge detection algorithm to segment the target contour for the image itself at different scene gray values; Secondly, considering the differentiation of related factors, this paper carefully classifies the correlation factors based on the EfficientNet network. In order to ensure the rapid detection of the model. Experimental results show that the accuracy of the proposed model reaches 97.1%, and the speed is also faster. This shows the superiority of the algorithm model proposed in this paper.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jing Yang, Mingyong Xin, and Qihui Feng "Automatic hot spot edge-detection method for photovoltaic aerial infrared image", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129231O (8 November 2023); https://doi.org/10.1117/12.3011341

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