Detection Method of Photovoltaic Panel Defect Based on Improved Mask R-CNN,ERICDATA高等教育知識庫
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篇名
Detection Method of Photovoltaic Panel Defect Based on Improved Mask R-CNN
並列篇名
Detection Method of Photovoltaic Panel Defect Based on Improved Mask R-CNN
作者 Shuqiang GuoZhiheng WangYue LouXianjin LiHuanqiang Lin
英文摘要

To solve the low efficiency and precision of uncrewed inspection in photovoltaic power stations, a segmentation method of improving the defective photovoltaic panels based on improved Mask R-CNN is proposed. The atrous spatial pyramid pooling and spatial attention mechanism were introduced into the extraction network to improve detection accuracy. Uncrewed aerial vehicle infrared video of the panels is used to input the network model for defect detection. As a result, the automatic annotation of the defect position is achieved, significantly improving the efficiency and precision of uncrewed inspection. The precision, recall and FPS are used as performance metrics to evaluate U-Net, PSPNet, Mask R-CNN and the algorithm in this paper. Experiments show that the detection precision of the four models are 77.3%, 82.2%, 84.0% and 89.8% respectively, the recall are 79.4%, 79.0%, 81.6% and 84.4% respectively, and the FPS are 12.5, 9.6, 8.6 and 8.2 respectively. Although the FPS of this algorithm is slightly reduced, the precision and recall have been greatly improved, and can be applied to industry.

 

起訖頁 397-406
關鍵詞 Photovoltaic panel defect detectionMask R-CNNAtrous spatial pyramidSpatial attention
刊名 網際網路技術學刊  
期數 202203 (23:2期)
出版單位 台灣學術網路管理委員會
DOI 10.53106/160792642022032302018  複製DOI
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