基于Yolov5的光伏缺陷检测方法、装置、计算机设备及介质
After labeling and registering the infrared and visible light image data of photovoltaic modules, the YOLOv5 target detection model is used for feature extraction and classification, which solves the problem that existing technologies cannot identify specific defect types of photovoltaic modules and realizes detailed defect detection and classification.
CN116228670BActive Publication Date: 2026-07-17SHENZHEN LAUNCH DIGITAL TECH
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN LAUNCH DIGITAL TECH
- Filing Date
- 2023-01-03
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing photovoltaic defect detection methods cannot accurately identify the types of faults, especially the specific types of defects in photovoltaic modules.
Method used
By acquiring infrared and visible light image data of photovoltaic modules, temperature anomalies and image anomalies are labeled and registered, and then input into the YOLOv5 target detection model for feature extraction and classification, detailed defect information of photovoltaic modules is output.
Benefits of technology
It enables detailed classification of defects in photovoltaic modules, improving the accuracy and reliability of detection and facilitating subsequent maintenance and cleaning.
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Figure CN116228670B_ABST
Abstract
本申请提供一种基于Yolov5的光伏缺陷检测方法，通过将所述配准后的图像数据集、所述标注后的红外图像数据集和所述标注后的可见光图像数据集输入Yolov5目标检测模型进行处理，并输出光伏组件缺陷分类详细信息，从而使得对红外图像及可见光图像进行分析，并对结果进行分类输出详细的光伏组件缺陷分类详细信息，便于后期工作人员依据详细的光伏组件缺陷分类详细信息进行维修或清理。
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