| Citation: | FAN Tao, SUN Tao, LIU Huet al. Hot spot detection algorithm of photovoltaic module based on attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(7): 1304-1313. doi: 10.13700/j.bh.1001-5965.2021.0457(in Chinese)
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The hot spot phenomenon is one of the important reasons for the reduction of power generation capacity of photovoltaic panels, and the detection of hot spots is an essential task for operation and maintenance personnel. The scale of distributed photovoltaic power plants is generally small, the site is scattered, the environment is complex and diverse, and the operation and maintenance personnel need to invest a lot of human resources to detect hot spots using traditional hot spot detection methods. In this paper, we propose a new hot spot detection algorithm HSNet. Firstly, the influence of reflection is eliminated through image segmentation. Secondly, the feature information between channels is learned in combination with the channel attention mechanism to enhance the importance of the target area. The method of user-defined anchor points is used to improve the detection speed. Then, the focus loss activation function and the category prediction method based on the prior probability of objects are used to improve the problem of low classification accuracy caused by the imbalance of training target samples, Finally, the accurate target position is obtained by regression method. Experiments show that the target detection algorithm designed in this paper has significant advantages over other algorithms in terms of window regression accuracy and classification accuracy, and the mean accuracy and accuracy of the bounding box are improved by 3.18% and 2.42%, respectively.
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