Automatic hotspots detection based on UAV infrared images for large‐scale PV plant. Issue 19 (28th July 2020)

• Record Type: Journal Article
• Authors: Nie, Junfei; Luo, Ting; Li, Hui
• Is Part Of: Electronics letters. Volume 56:Issue 19(2020)
• Page Start: 993
• Page End: 995
• Publication Date: 2020-07-28
• Subjects: autonomous aerial vehicles -- inspection -- infrared imaging -- computer vision -- image segmentation -- feature extraction -- photovoltaic power systems -- learning (artificial intelligence) -- image processing -- remotely operated vehicles; hotspot feature -- deep‐learning‐based model -- automatic hotspot detection -- line segments detection -- defected feature -- infrared image -- traditional image processing method -- large‐scale photovoltaic plant -- unmanned aerial vehicle -- intelligent inspection -- manual inspection -- photovoltaic power station -- maintenance -- solar energy -- large‐scale PV plant -- automatic hotspots detection; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2020.1542
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Owing to the significantly installed capacity of solar energy during the past decades, the operation and maintenance of the large photovoltaic power station is a big challenge, as the manual inspection is labour‐intensive and expensive. This Letter presents a solution for the intelligent inspection of the hotspot with the unmanned aerial vehicle in the large‐scale photovoltaic plant. First, a traditional image processing method is presented to eliminate the noise and crop the infrared image, which can make the defected feature more obvious. Then, the module is extracted by the line segments detection. Finally, considering the great advance in the realm of the computer vision, a deep‐learning‐based method for automatic hotspot detection is proposed for locating the hotspot with the data augmentation. The deep‐learning‐based model can extract the hotspot feature by the training process in the data set. In the end, the performance of the method proposed is extensively evaluated and the numerical results prove the accuracy and precision of the model.