YOLO v2를 이용한 고해상도 항공영상에서의 태양광발전소 탐지 방법 연구
A Study on the Detection of Solar Power Plant for High-Resolution Aerial Imagery Using YOLO v2
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초록
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.
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.
참고문헌 (22)
- Akihiko Ishikawa, "Deep Learning Textbook to Learn using Python, Hanbit media", ~ (2020)
- Al-Mashhadani, R., "Deep learning methods for solar fault detection and classification : A review", Information Sciences Letters, 10(2), pp.13~ (2021)[DOI]
- 진경일, "태양광 발전사업 부지조성을 위한 농촌지역개발 행위의 문제점 분석 및 개선방안", KIEAE Journal, 21(5), pp.83~90 (2021), 한국생태환경건축학회[DOI]
- "Solar Power Project Permit Status" (2021)[URL]
- "Solar Power Project Permit Status" (2021)[URL]
- "Generation capacity by fuel" (2021)[URL]
- Golovko, V., "Development of solar panels detector", 2018International Scientific- Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), pp.761~764 (2018), IEEE
- "Gorlapraveen123" (2021)[URL]
- 하지영, "농촌태양광 이슈에 대한 소셜미디어 게시글과 이용자 댓글 분석", Journal of The Korean Data Analysis Society, 24(2), pp.683~699 (2022), 한국자료분석학회[DOI]
- Han, S. H., "Damaged solar panel detection drone system using deep learning and thermal imaging camera", Conference proceeding of The Korean Institute of Communications and Information Sciences (2020)
- "Solar Power Project Permit Status" (2019)[URL]
- "Solar Power Project Permit Status" (2018)[URL]
- 조원호, "합성곱 신경망을 이용한 딥러닝 기반의 토지피복 분류: 한국 토지피복을 대상으로", 대한지리학회지, 54(1), pp.1~16 (2019), 대한지리학회
- "Kakao-skyview"[URL]
- 김준석, "YOLO 신경망 기반의 UAV 영상을 이용한 건물 객체 탐지 분석", 한국측량학회지, 39(6), pp.381~392 (2021), 한국측량학회[DOI]
- Research on problems and improvement plans for the spread of rural solar power, "Korea Rural Economic Institute(KREI)", KREI, ~ (2018)
- 이성혁, "딥러닝 기반의 영상분할을 이용한 토지피복분류", 대한원격탐사학회지, 35(2), pp.279~288 (2019), 대한원격탐사학회[DOI]
- "MathworksⒸ"[URL]
- Park, M. -L., "Survey and Analysis of Resident Acceptability for Photovoltaic System in Rural Region-Focusing on Economic Factors", Conference proceeding of the Korean solar energy society, pp.198~ (2019)
- Pérez, R. M., "Solar panels recognition based on machine learning", 2019 IV Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI), pp.1~5 (2019), IEEE
- Planche, B., "Practice! Deep Learning Computer Vision with TensorFlow 2" (2020), Wikibooks
- SaGong, J. H., "Current status of solar power generation facilities in Chungnam and ecological and landscape response strategies", Chungnam Report, 295, pp.1~13 (2018)[DOI]