A deep learning-based solar photovoltaic panel component extraction system and method

CN116721340BActive Publication Date: 2026-02-27NINGBO BODEN AI TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202310512842.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-05-08
Publication Date
2026-02-27
Estimated Expiration
2043-05-08

AI Technical Summary

Technical Problem

In existing technologies, methods for extracting solar photovoltaic panel components are easily affected by environmental factors such as light, shadow and weather, and require a large amount of manpower and have limited ability to recognize complex scenes, resulting in poor extraction results.

Method used

Employing a deep learning-based approach, the model is trained using data from various lighting and weather conditions. Combining the physical characteristics of photovoltaic panels with computer vision image enhancement, the system can quickly identify and separate solar photovoltaic panel components. The process includes modules such as data preprocessing, model training, data uploading, string extraction, and result analysis.

Benefits of technology

It improves the accuracy and generalization of solar photovoltaic panel component extraction, reduces manual intervention, adapts to various task requirements, has high detection accuracy and low false detection rate, and can iteratively update the same batch of data.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses a kind of solar photovoltaic panel assembly extraction systems based on deep learning, it is related to the field of deep learning, containing data labeling module, model generation module, data upload module, group string extraction module, component extraction module, result analysis export module, user control module;The application also discloses a kind of solar photovoltaic panel assembly extraction method based on deep learning, including S100, photovoltaic panel image manual labeling, S200, solar photovoltaic panel assembly extraction model training, S300, input image and up-sampling, S400, group string extraction and result filtering, S500, image processing and component extraction, S600, extraction result export.The application can quickly identify and separate photovoltaic panel in image through image processing task, improve detection efficiency, extraction precision is high, and generalization is strong.
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Citation Information

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