View in EDS

Higher accuracy detection strategy for electroluminescent defects in photovoltaic modules based on improved Yolov5.

Saved in:
Bibliographic Details
Title: Higher accuracy detection strategy for electroluminescent defects in photovoltaic modules based on improved Yolov5.
Authors: Ding, Sheng1 (AUTHOR), Chen, Tiansheng1 (AUTHOR), Chen, Hao1 (AUTHOR), Chen, Congyan1 (AUTHOR) chency@seu.edu.cn
Source: IET Renewable Power Generation (Wiley-Blackwell). Oct2023, Vol. 17 Issue 14, p3582-3593. 12p.
Subjects: Solar cells, Environmental quality, Photovoltaic cells
Abstract: At present, the domestic photovoltaic (PV) industry is developing rapidly. In order to improve the production efficiency of PV cells, a fast and accurate automatic detection model of PV modules' defects that can be applied in the production line is essential. In this paper, based on the characteristics of significant differences in PV module defect size and a large number of fine defects, an improved defect detection algorithm based on Yolov5 is proposed. Thirteen mainstream defects are divided into two categories according to size, and a series‐connected detection network is constructed for a two‐stage detection. In order to better detect fine defects, this paper proposes the TR‐ResNet module, a residual module composed of the self‐attention, based on the self‐attention mechanism, to replace some fully convolutional residual modules (CNN‐ResNet module) in the Yolov5 backbone network. After testing, the Precision, Recall and mAP of the model are greatly improved and gained 0.904, 0.845, and 0.840. Moreover, the model performs well in stability detection, which can adapt to different production environments and quality requirements. The present study may make the detection work more efficient and improve productivity. [ABSTRACT FROM AUTHOR]
Copyright of IET Renewable Power Generation (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:At present, the domestic photovoltaic (PV) industry is developing rapidly. In order to improve the production efficiency of PV cells, a fast and accurate automatic detection model of PV modules' defects that can be applied in the production line is essential. In this paper, based on the characteristics of significant differences in PV module defect size and a large number of fine defects, an improved defect detection algorithm based on Yolov5 is proposed. Thirteen mainstream defects are divided into two categories according to size, and a series‐connected detection network is constructed for a two‐stage detection. In order to better detect fine defects, this paper proposes the TR‐ResNet module, a residual module composed of the self‐attention, based on the self‐attention mechanism, to replace some fully convolutional residual modules (CNN‐ResNet module) in the Yolov5 backbone network. After testing, the Precision, Recall and mAP of the model are greatly improved and gained 0.904, 0.845, and 0.840. Moreover, the model performs well in stability detection, which can adapt to different production environments and quality requirements. The present study may make the detection work more efficient and improve productivity. [ABSTRACT FROM AUTHOR]
ISSN:17521416
DOI:10.1049/rpg2.12856