Enhanced Parallel Convolution Architecture YOLO Photovoltaic Panel Detection Model for Remote Sensing Images

Jinsong Li, Xiaokai Meng, Shuai Wang, Zhumao Lu, Hua Yu, Zeng Qu and Jiayun Wang ()
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Jinsong Li: State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, China
Xiaokai Meng: State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, China
Shuai Wang: State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, China
Zhumao Lu: State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, China
Hua Yu: State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, China
Zeng Qu: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
Jiayun Wang: School of Instrument and Electronics, North University of China, Taiyuan 030051, China

Sustainability, 2025, vol. 17, issue 14, 1-15

Abstract: Object detection technology enables the automatic identification of photovoltaic (PV) panel locations and conditions, significantly enhancing operational efficiency for maintenance teams while reducing the time and cost associated with manual inspections. Challenges arise due to the low resolution of remote sensing images combined with small-sized targets—PV panels intertwined with complex urban or natural backgrounds. To address this, a parallel architecture model based on YOLOv5 was designed, substituting traditional residual connections with parallel convolution structures to enhance feature extraction capabilities and information transmission efficiency. Drawing inspiration from the bottleneck design concept, a primary feature extraction module framework was constructed to optimize the model’s deep learning capacity. The improved model achieved a 4.3% increase in mAP, a 0.07 rise in F1 score, a 6.55% enhancement in recall rate, and a 6.2% improvement in precision. Additionally, the study validated the model’s performance and examined the impact of different loss functions on it, explored learning rate adjustment strategies under various scenarios, and analyzed how individual factors affect learning rate decay during its initial stages. This research notably optimizes detection accuracy and efficiency, holding promise for application in large-scale intelligent PV power station maintenance systems and providing reliable technical support for clean energy infrastructure management.

Keywords: photovoltaic panels; object detection; deep learning; parallel convolution; remote sensing images (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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