AN EFFICIENT DUAL-PATH ATTENTION SOLAR CELL DEFECT DETECTION NETWORK

Zhou Ying; Wang Ruyi; Yuan Zitong; Liu Kun; Chen Haiyong

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

PDF(2357 KB)

Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (4) : 407-413. DOI: 10.19912/j.0254-0096.tynxb.2021-1400

AN EFFICIENT DUAL-PATH ATTENTION SOLAR CELL DEFECT DETECTION NETWORK

Zhou Ying^1,2, Wang Ruyi¹, Yuan Zitong¹, Liu Kun^1,2, Chen Haiyong^1,2

Author information +

History +

Abstract

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.

Key words

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

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Zhou Ying, Wang Ruyi, Yuan Zitong, Liu Kun, Chen Haiyong. AN EFFICIENT DUAL-PATH ATTENTION SOLAR CELL DEFECT DETECTION NETWORK[J]. Acta Energiae Solaris Sinica. 2023, 44(4): 407-413 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1400

References

[1] JEN C Y, RICHTER C.Doping profile recognition applied to silicon photovoltaic cells using terahertz time-domain spectroscopy[J]. IEEE transactions on terahertz science and technology, 2014, 4(5): 560-567.
[2] 李洁, 袁知博, 秦嘉悦. 基于Sobel算子边缘检测的太阳电池缺陷特征提取方法[J]. 太阳能学报, 2021, 42(1): 63-68.
LI J, YUAN Z B, QIN J Y.Research on solar cells defects feature extraction based on Sobel operator edge detection[J]. Acta energiae solaris sinica, 2021, 42(1): 63-68.
[3] 王理顺, 钟勇, 李振东, 等. 基于深度学习的织物缺陷在线检测算法[J]. 计算机应用, 2019, 39(7): 2125-2128.
WANG L S, ZHONG Y, LI Z D, et al.On-line fabric defect recognition algorithm based on deep learning[J]. Journal of computer applications, 2019, 39(7): 2125-2128.
[4] 李林升, 曾平平. 改进深度学习框架Faster-RCNN的苹果目标检测[J]. 机械设计与研究, 2019, 35(5): 24-27.
LI L S, ZENG P P.Apple target detection based on improved Faster-RCNN framework of deep learning[J]. Machine design & research, 2019, 35(5): 24-27.
[5] 孙彦, 丁学文, 雷雨婷. 基于目标检测模型的人脸识别技术[J]. 计算机与网络, 2019, 45(22): 68-71.
SUN Y, DING X W, LEI Y T.Face recognition technology based on target detection model[J]. Computers & network, 2019, 45(22): 68-71.
[6] 顾佳晨, 高雷, 刘路璐. 基于深度学习的目标检测算法在冷轧表面缺陷检测中的应用[J]. 冶金自动化, 2019, 43(6): 19-22, 27.
GU J C, GAO L, LIU L L.Application of object detection algorithm based on deep learning for inspection of surface defect of cold rolled strips[J]. Metallurgical industry automation, 2019, 43(6): 19-22, 27.
[7] HOU W, TAO X, XU D.Combining prior knowledge with CNN for weak scratch inspection of optical components[J]. IEEE transactions on instrumentation and measurement, 2021, 70: 1-11.
[8] ZHANG X, HAO Y W, SHANGGUAN H, et al.Detection of surface defects on solar cells by fusing multi-channel convolution neural networks[J]. Infrared physics & technology, 2020, 108: 103334.
[9] CHEN H Y, PANG Y, HU Q D, et al.Solar cell surface defect inspection based on multispectral convolutional neural network[J]. Journal of intelligent manufacturing, 2020, 31(2): 453-468.
[10] SU B Y, 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, 2021, 17(6): 4084-4095.
[11] TAO X, XU D, ZHANG Z T, et al.Weak scratch detection and defect classification methods for a large-aperture optical element[J]. Optics communications, 2017, 387: 390-400.
[12] YI X, SONG Y H, TANG X.Weak supervised surface defect detection method based on selective search and CAM[C]// 2019 Chinese Automation Congress(CAC), IEEE Hangzhou, China, 2019: 4386-4391.
[13] LI W, LIU K, ZHANG L Z, et al.Object detection based on an adaptive attention mechanism[J]. Scientific reports, 2020, 10(1): 11307.