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

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.