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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.
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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Header DbId: enr
DbLabel: Energy & Power Source
An: 194141534
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: Autonomous Inspection Technology for Ultra-Large-Scale Photovoltaic Panels Based on AI Vision.
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  Data: <searchLink fieldCode="AR" term="%22Gong%2C+Quanhua%22">Gong, Quanhua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Khan%2C+Muhammad+Imran%22">Khan, Muhammad Imran</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> liushuhai@tongji.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Shuhai%22">Liu, Shuhai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xie%2C+Liquan%22">Xie, Liquan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> xie_liquan@tongji.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2419. 20p.
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  Data: *<searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Robot+vision%22">Robot vision</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br />*<searchLink fieldCode="DE" term="%22Photovoltaic+power+systems%22">Photovoltaic power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Quality+control%22">Quality control</searchLink><br />*<searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br />*<searchLink fieldCode="DE" term="%22Photovoltaic+power+generation%22">Photovoltaic power generation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194141534
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        Value: 10.3390/en19102419
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        Text: English
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      – SubjectFull: Robot vision
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      – SubjectFull: Renewable energy sources
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      – SubjectFull: Quality control
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      – SubjectFull: Remote sensing
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      – SubjectFull: Photovoltaic power generation
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      – TitleFull: Autonomous Inspection Technology for Ultra-Large-Scale Photovoltaic Panels Based on AI Vision.
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            NameFull: Gong, Quanhua
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            NameFull: Khan, Muhammad Imran
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            NameFull: Liu, Shuhai
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            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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