AN EFFICIENT DUAL-PATH ATTENTION SOLAR CELL DEFECT DETECTION NETWORK

被引：0

作者：

Zhou Y. ^{[1
,2
]}

Wang R. ^{[1
]}

Yuan Z. ^{[1
]}

Liu K. ^{[1
,2
]}

Chen H. ^{[1
,2
]}

机构：

[1] School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin

[2] Hebei Control Engineering Technology Research Center, Tianjin

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 04期

关键词：

attention mechanism; convolutional neural network; multi-scale fusion; object detection; solar cells;

D O I：

10.19912/j.0254-0096.tynxb.2021-1400

中图分类号：

学科分类号：

摘要：

Absrtact：Aiming at the characteristics of different defect scales，large span，complex surface texture background and small defects of polycrystalline silicon solar cells，a new detection framework EDANet is proposed based on Yolov4，and two new modules are constructed：cross-scale group space enhancement（CGSE）module and self-calibrated squeeze and excitation（SCSE）module. CGSE module integrates features，as spatial attention，in the form of multi- scale，suppresses background，highlights foreground，and reweights feature maps and guides the network to learn the correct foreground and background feature distribution. The SCSE module establishes a long-distance dependence on each spatial location of the high-level feature in the form of channel attention，and helps the network to generate more identification representations by explicitly merging information to distinguish small and weak defects. The experimental results show that the mAP value of the network reaches 92.07%，and the accuracy of defect detection of polycrystalline silicon solar cells is significantly improved. © 2023 Science Press. All rights reserved.

引用

页码：407 / 413

页数：6

共 13 条

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