Autonomous Inspection Technology for Ultra-Large-Scale Photovoltaic Panels Based on AI Vision.
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| Title: | Autonomous Inspection Technology for Ultra-Large-Scale Photovoltaic Panels Based on AI Vision. |
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| Authors: | Gong, Quanhua1 (AUTHOR), Khan, Muhammad Imran2 (AUTHOR) liushuhai@tongji.edu.cn, Liu, Shuhai1,2 (AUTHOR), Xie, Liquan2 (AUTHOR) xie_liquan@tongji.edu.cn |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 10, p2419. 20p. |
| Subject Terms: | *Object recognition (Computer vision), *Deep learning, *Robot vision, *Renewable energy sources, *Photovoltaic power systems, *Quality control, *Remote sensing, *Photovoltaic power generation |
| Abstract: | Ultra-large-scale offshore photovoltaic (PV) installations require efficient and reliable construction-phase inspection to ensure installation integrity and compliance with engineering specifications. As the deployment scale expands to thousands of platforms and millions of photovoltaic modules, conventional manual inspection becomes labor-intensive, time-consuming, and increasingly prone to omission errors. This study presents an autonomous inspection framework based on AI-driven computer vision for the detection and localization of missing photovoltaic modules in offshore PV systems. The proposed framework integrates high-resolution UAV-acquired RGB imagery, YOLOv8-based object detection, geographic coordinate transformation, spatial deduplication, and deterministic grid-based indexing to convert aerial observations into structured engineering inspection records. Each detected missing module is automatically assigned a unique platform identifier together with row–column coordinates, enabling engineering-level localization while eliminating redundant detections caused by overlapping UAV imagery. The proposed framework was validated using a dataset comprising 2800 annotated UAV images collected from a 1 GW offshore photovoltaic project. The experimental results revealed a recall of 96.15%, an F1-score of 98.04%, and a manual verification consistency of 96.83%. Geographic deduplication eliminated duplicate grid records, while the average processing time of 1.12 s per image demonstrates the computational feasibility of the framework for large-scale offshore deployment. The results confirm that integrating deep learning-based visual detection with geographic spatial mapping enables reliable, scalable, and engineering-oriented verification of missing photovoltaic modules during construction-phase inspection, thereby supporting standardized and data-driven acceptance workflows for large-scale renewable energy infrastructure. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Ultra-large-scale offshore photovoltaic (PV) installations require efficient and reliable construction-phase inspection to ensure installation integrity and compliance with engineering specifications. As the deployment scale expands to thousands of platforms and millions of photovoltaic modules, conventional manual inspection becomes labor-intensive, time-consuming, and increasingly prone to omission errors. This study presents an autonomous inspection framework based on AI-driven computer vision for the detection and localization of missing photovoltaic modules in offshore PV systems. The proposed framework integrates high-resolution UAV-acquired RGB imagery, YOLOv8-based object detection, geographic coordinate transformation, spatial deduplication, and deterministic grid-based indexing to convert aerial observations into structured engineering inspection records. Each detected missing module is automatically assigned a unique platform identifier together with row–column coordinates, enabling engineering-level localization while eliminating redundant detections caused by overlapping UAV imagery. The proposed framework was validated using a dataset comprising 2800 annotated UAV images collected from a 1 GW offshore photovoltaic project. The experimental results revealed a recall of 96.15%, an F1-score of 98.04%, and a manual verification consistency of 96.83%. Geographic deduplication eliminated duplicate grid records, while the average processing time of 1.12 s per image demonstrates the computational feasibility of the framework for large-scale offshore deployment. The results confirm that integrating deep learning-based visual detection with geographic spatial mapping enables reliable, scalable, and engineering-oriented verification of missing photovoltaic modules during construction-phase inspection, thereby supporting standardized and data-driven acceptance workflows for large-scale renewable energy infrastructure. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19102419 |
