Enhancing Rooftop Photovoltaic Segmentation Using Spatial Feature Reconstruction and Multi-Scale Feature Aggregation

Yu Xiao, Long Lin, Jun Ma and Maoqiang Bi ()
Additional contact information
Yu Xiao: School of Electric Power Engineering, Chongqing Water Resources and Electric Engineering College, Chongqing 404155, China
Long Lin: School of Electric Power Engineering, Chongqing Water Resources and Electric Engineering College, Chongqing 404155, China
Jun Ma: School of Electric Power Engineering, Chongqing Water Resources and Electric Engineering College, Chongqing 404155, China
Maoqiang Bi: School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 404155, China

Energies, 2024, vol. 18, issue 1, 1-19

Abstract: Amidst the dual challenges of energy shortages and global warming, photovoltaic (PV) power generation has emerged as a critical technology due to its efficient utilization of solar energy. Rooftops, as underutilized spaces, are ideal locations for installing solar panels, avoiding the need for additional land. However, the accurate and generalized segmentation of large-scale PV panel images remains a technical challenge, primarily due to varying image resolutions, large image scales, and the significant imbalance between foreground and background categories. To address these challenges, this paper proposes a novel model based on the Res2Net architecture, an enhanced version of the classic ResNet optimized for multi-scale feature extraction. The model integrates Spatial Feature Reconstruction and multi-scale feature aggregation modules, enabling effective extraction of multi-scale data features and precise reconstruction of spatial features. These improvements are particularly designed to handle the small proportion of PV panels in images, effectively distinguishing target features from redundant ones and improving recognition accuracy. Comparative experiments conducted on a publicly available rooftop PV dataset demonstrate that the proposed method achieves superior performance compared to mainstream techniques, showcasing its effectiveness in precise PV panel segmentation.

Keywords: spatial feature reconstruction; multi-scale feature fusion; PV detection; Res2Net (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/1/119/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/1/119/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2024:i:1:p:119-:d:1557481

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().