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http://dx.doi.org/10.7851/ksrp.2022.28.2.087

A Study on the Detection of Solar Power Plant for High-Resolution Aerial Imagery Using YOLO v2  

Kim, Hayoung (Department of Rural and Biosystems Engineering & BK21 Education and Research Unit for Climate-smart Reclaimed-Tideland Agriculture, Chonnam National University)
Na, Ra (Department of Rural and Biosystems Engineering & BK21 Education and Research Unit for Climate-smart Reclaimed-Tideland Agriculture, Chonnam National University)
Joo, Donghyuk (Department of Rural and Biosystems Engineering & BK21 Education and Research Unit for Climate-smart Reclaimed-Tideland Agriculture, Chonnam National University)
Choi, Gyuhoon (WeDB)
Oh, Yun-Gyeong (Institute of Agricultural Science & Technology, Chonnam National University)
Publication Information
Journal of Korean Society of Rural Planning / v.28, no.2, 2022 , pp. 87-96 More about this Journal
Abstract
As part of strengthening energy security and responding to climate change, the government has promoted various renewable energy measures to increase the development of renewable energy facilities. As a result, small-scale solar installations in rural areas have increased rapidly. The number of complaints from local residents is increasing. Therefore, in this study, deep learning technology is applied to high-resolution aerial images on the internet to detect solar power plants installed in rural areas to determine whether or not solar power plants are installed. Specifically, I examined the solar facility detector generated by training the YOLO(You Only Look Once) v2 object detector and looked at its usability. As a result, about 800 pieces of training data showed a high object detection rate of 93%. By constructing such an object detection model, it is expected that it can be utilized for land use monitoring in rural areas, and it can be utilized as a spatial data construction plan for rural areas using technology for detecting small-scale agricultural facilities.
Keywords
Deep learning; Solar power plant; Land-use; Aerial Image; YOLO v2;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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