TransPV: Refining photovoltaic panel detection accuracy through a vision transformer-based deep learning model

Authors:
raw_author_name: Zhiling Guo
raw_affiliation_string: Center for Spatial Information Science, University of Tokyo, Kashiwa 277-8568, Japan; Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
raw_author_name: Jiayue Lu
raw_affiliation_string: Graduate School of Information, Production and Systems, Waseda University, Kitakyushu City, Fukuoka, Japan
raw_author_name: Qi Chen
raw_affiliation_string: China School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
raw_author_name: Zhengguang Liu
raw_affiliation_string: Department of Power and Electrical Engineering, Northwest A&F University, Yangling 712100, China
raw_author_name: Hongjun Tan
raw_affiliation_string: Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
raw_author_name: Haoran Zhang
raw_affiliation_string: School of Urban Planning and Design, Peking University, No.2199 Lishui Road, Nanshan District, Shenzhen, Guangdong 518055, China
raw_author_name: Jinyue Yan
raw_affiliation_string: Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China

Abstract: The increasing need to develop renewable energy sources to combat climate change has led to a significant rise in demand for photovoltaic (PV) installations. Consequently, accurately detecting and estimating the capacity and potential for electricity generation of these installed PV systems has become crucial for effective energy management. However, due to the limitation of localized mechanism in convolution operations, traditional FCN-based methods face challenges in capturing global dependencies among remote sensing image patches. As a result, they struggle to model long-range interactions between different PV panels with diverse structures, including varying size, shape, and texture, particularly in distributed residential areas. To tackle the challenge of modeling PV panels with diverse structures, we propose a coupled U-Net and Vision Transformer model named TransPV for refining PV semantic segmentation. Specifically, Mix Transformer block is incorporated in the encoder to enhance the modeling of global context, while the U-Shaped structure enables the combination of multi-level features, resulting in enriched feature representation, which helps the model better understand objects in the images, regardless of their shape, size, and texture. In addition, we implement the PointRend module in the decoder to obtain finer segmentation boundary details, and utilize a novel refiner loss function during the training process to alleviate the problem of extremely unbalanced samples. The experimental results from the Heilbronn datasets demonstrate the remarkable performance of our proposed TransPV model in addressing intra-class diversity of PV structures, surpassing previous state-of-the-art methods with an impressive IoU and accuracy of 0.802 and 0.876, respectively. Furthermore, our model exhibits high generalization capability on the BDPV dataset with IoU of 0.745. The obtained results highlight the superiority of TransPV in improving accuracy and addressing diverse structure issues in PV segmentation, which provides valuable insights for optimizing the efficiency and sustainability of renewable energy.