Xu Han, Xianhao Wang, Chong Chen, Gong Li, Changhao Piao, Journal of Information Processing Systems Vol. 19, No. 6, pp. 791-802, Dec. 2023  

10.3745/JIPS.02.0208
Keywords: DeepLab V3+, Geometric Location, Hot Spot Location, Hot Spot Recognition, YOLO v3
Fulltext: PDF Full Text PubReader

Abstract

The manual inspection of photovoltaic (PV) panels to meet the requirements of inspection work for large-scale PV power plants is challenging. We present a hot spot detection and positioning method to detect hot spots in batches and locate their latitudes and longitudes. First, a network based on the YOLOv3 architecture was utilized to identify hot spots. The innovation is to modify the RU_1 unit in the YOLOv3 model for hot spot detection in the far field of view and add a neural network residual unit for fusion. In addition, because of the misidentification problem in the infrared images of the solar PV panels, the DeepLab v3+ model was adopted to segment the PV panels to filter out the misidentification caused by bright spots on the ground. Finally, the latitude and longitude of the hot spot are calculated according to the geometric positioning method utilizing known information such as the drone's yaw angle, shooting height, and lens field-of-view. The experimental results indicate that the hot spot recognition rate accuracy is above 98%. When keeping the drone 25 m off the ground, the hot spot positioning error is at the decimeter level.

Statistics
Cite this article