| Citation: | XIE Xiangying, LAI Guangzhi, NA Zhixiong, et al. Occlusion recognition algorithm based on multi-resolution feature auto-selection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(7): 1154-1163. doi: 10.13700/j.bh.1001-5965.2021.0289(in Chinese)
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The identification of obstructions of photovoltaic modules is an indispensable link in modern photovoltaic operation and maintenance systems. Traditional identification methods mostly rely on manual inspections, but they are costly and inefficient. Therefore, based on the convolutional neural network, PORNet, an occlusion recognition algorithm for photovoltaic modules, is proposed. By introducing feature pyramids, image features with rich semantic information at multiple resolutions are constructed, enhancing the sensitivity to the scale and density of occlusions. Through feature auto-selection, the most representative feature maps are screened out to strengthen the semantic information expression of the object contexts. Finally, the screened feature map is used to complete the occlusion recognition, improving the recognition accuracy. Experimental comparison and analysis are carried out on the self-built photovoltaic module falling leaf occlusion dataset, and the recognition performance is evaluated. Compared with existing object recognition methods, the accuracy and recall rate of the proposed method are increased by 9.21% and 15.79%, respectively.
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