Infrared image photovoltaic panel defect detection method under unmanned aerial vehicle perspective and related device

By using an infrared image detection method from the perspective of drones, infrared images of photovoltaic panels are acquired by drones, preprocessed and feature extracted, and combined with a neural network model to identify defects. This solves the problems of high efficiency, accuracy, safety and low cost in the detection of photovoltaic panels in the existing technology, and realizes rapid operation and maintenance of photovoltaic power plants.

CN122244726APending Publication Date: 2026-06-19HUANENG ANHUI MENGCHENG WIND POWER CO LTD +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUANENG ANHUI MENGCHENG WIND POWER CO LTD
Filing Date
2026-03-09
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Existing photovoltaic panel defect detection technologies struggle to balance efficiency, accuracy, safety, and low cost, failing to meet the routine and timely operation and maintenance needs of photovoltaic power plants.

⚗Method used

An infrared image detection method from the perspective of drones is adopted. Infrared images of photovoltaic panels are acquired by drones, preprocessed and feature extracted, and defect types are identified by a trained neural network model. Combined with a path planning algorithm, automatic navigation and detection are achieved.

🎯Benefits of technology

It enables efficient, accurate, safe, and low-cost photovoltaic panel defect detection, supports rapid operation and maintenance of large-scale photovoltaic power plants, and improves the accuracy and efficiency of detection.

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Abstract

This invention discloses a method and related apparatus for detecting defects in photovoltaic panels using infrared images from the perspective of a drone. The method includes: acquiring infrared images of photovoltaic panels using a drone; preprocessing the infrared images of the photovoltaic panels to obtain preprocessed infrared images; extracting features from the preprocessed infrared images; and inputting the extracted features into a trained neural network model to determine the defect type of the photovoltaic panel. This method and related apparatus can efficiently, accurately, safely, and cost-effectively detect defects in photovoltaic panels.
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