PHOTOVOLTAIC DEFECT DETECTION BASED ON MULTI-SCALE CODING COMPLEMENTARY ATTENTION NETWORK

被引：0

作者：

Chen H. ^{[1
]}

Yuan L. ^{[1
]}

Wang S. ^{[1
]}

Zhao S. ^{[1
]}

机构：

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

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 10期

关键词：

complementarycoordinate attention; convolutionalneuralnetwork; defectdetection; multi-scaleencoder; photovoltaicmodules;

D O I：

10.19912/j.0254-0096.tynxb.2022-0966

中图分类号：

学科分类号：

摘要：

The defect detection for photovoltaic module electroluminescence images is a challenging task，due to two difficulties，tiny and weak. To address this problem，the Multi- Scale Encoding Complementary Attention Network （MCECAN） is designed. The backbone and prediction head of MCECAN follow the YOLO series design，but the network neck applies the Multi- Scale Coding Complementary Attention Module （MCECAM）. The front- end of the module uses a multi- scale encoder to aggregate multi- scale information and enhance global information. The back- end complementary coordinate attention establishes the dependency between feature map channels，highlights defect features，suppresses background interference，and improves the ability of network to detect tiny and weak targets. On a dataset containing 5537 EL defect images，the MCECAN shows the best detection performance. © 2023 Science Press. All rights reserved.

引用

页码：191 / 197

页数：6

共 19 条

[1] ZHOU Y, YE H, WANG T, Et al., Photovoltaic module defect identification based on multi- scale convolution neural network［J］, Acta energiae solaris sinica, 43, 2, pp. 211-216, (2022)
[2] CHEN H Y，, CHEN P，, Et al., Deep learning-based solar-cell manufacturing defect detection with complementary attention network［J］, IEEE transactions on industrial informatics, 17, 6, pp. 4084-4095, (2021)
[3] CHEN H Y，, ZHOU Z., BAF-detector：an efficient CNN- based detector for photovoltaic cell defect detection ［J］, IEEE transactions on industrial electronics, 69, 3, pp. 3161-3171, (2022)
[4] TAO Z Y，, DU F T，, REN X K, Et al., Defect detection of solar cells based on T- VGG［J］, Acta energiae solaris sinica, 43, 7, pp. 145-151, (2022)
[5] ZHOU Y, WANG R Y，, YUAN Z T, Et al., An efficient dual-path attention solar cell defect detection network［J］, Acta energiae solaris sinica, 44, 4, pp. 407-413, (2023)
[6] You only look once： unified， real-time object detection［C］, 2016 IEEE Conference on Computer Vision and Pattern Recognition（CVPR）, pp. 779-788, (2016)
[7] Computer Vision-ECCV 2016, pp. 21-37, (2016)
[8] 2017 IEEE Conference on Computer Vision and Pattern Recognition（CVPR）, pp. 6517-6525, (2017)
[9] GOYAL P, Et al., Focal loss for dense object detection, 2017 IEEE International Conference on Computer Vision（ICCV）, pp. 2999-3007, (2017)
[10] IQA：visual question answering in interactive environments, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4089-4098, (2018)

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