• Optics and Precision Engineering
  • Vol. 32, Issue 14, 2286 (2024)
Ying ZHOU1,2,*, Shibo XU1, Haiyong CHEN1,2, and Kun LIU1,2
Author Affiliations
  • 1School of Artificial Intelligence, Hebei University of Technology, Tianjin30030, China
  • 2China Hebei Control Engineering Research Center, Tianjin300130, China
  • show less
    DOI: 10.37188/OPE.20243214.2286 Cite this Article
    Ying ZHOU, Shibo XU, Haiyong CHEN, Kun LIU. Solar cell defect detection network combining multiscale feature and attention[J]. Optics and Precision Engineering, 2024, 32(14): 2286 Copy Citation Text show less
    References

    [1] X Q ZHENG, S ZHENG, Y G KONG et al. Recent advances in surface defect inspection of industrial products using deep learning techniques. The International Journal of Advanced Manufacturing Technology, 113, 35-58(2021).

    [2] 赵朗月, 吴一全. 基于机器视觉的表面缺陷检测方法研究进展[J]. 仪器仪表学报, 2022, 43(1): 198-219.ZHAOL Y, WUY Q. Research progress of surface defect detection methods based on machine vision[J]. Chinese Journal of Scientific Instrument, 2022, 43(1): 198-219.(in Chinese)

    [3] 马桂艳, 张红妹, 史金超, 等. 基于电致发光的太阳能电池检测方法研究[J]. 光电子技术, 2020, 40(3): 213-216.MAG Y, ZHANGH M, SHIJ C, et al. Study of electroluminescence imaging as a method to detect the defects of solar cells[J]. Optoelectronic Technology, 2020, 40(3): 213-216.(in Chinese)

    [4] 夏中高, 彭平, 景彦姣, 等. EL检测PERC电池中间缺陷成因及改善方法的研究[J]. 太阳能学报, 2023, 44(6): 198-203.XIAZ G, PENGP, JINGY J, et al. Study on causes and improvement methods of intermediate defects in perc cells detected by el[J]. Acta Energiae Solaris Sinica, 2023, 44(6): 198-203.(in Chinese)

    [5] B Y SU, H Y CHEN, P CHEN et al. Deep learning-based solar-cell manufacturing defect detection with complementary attention network. IEEE Transactions on Industrial Informatics, 17, 4084-4095(2021).

    [6] M ZHANG, L J YIN. Solar cell surface defect detection based on improved YOLO v5. IEEE Access, 10, 80804-80815(2022).

    [7] S W XU, H M QIAN, W Y SHEN et al. Defect detection for PV Modules based on the improved YOLOv5s, 1431-1436(2022).

    [8] 张大胜, 肖汉光, 文杰, 等. YOLOv5上融合多特征的实时火焰检测方法[J]. 模式识别与人工智能, 2022, 35(6): 548-561.ZHANGD S, XIAOH G, WENJ, et al. Real-time fire detection method with multi-feature fusion on YOLOv5[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(6): 548-561.(in Chinese)

    [9] 陈海永, 袁乐, 王世杰, 等. 基于多尺度编码互补注意力网络的光伏缺陷检测[J]. 太阳能学报, 2023, 44(10): 191-197.CHENH Y, YUANL /Y), WANGS J, et al. Photovoltaic defect detection based on multi-scale coding complementary attention network[J]. Acta Energiae Solaris Sinica, 2023, 44(10): 191-197.(in Chinese)

    [10] N Y ZENG, P S WU, Z D WANG et al. A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection. IEEE Transactions on Instrumentation and Measurement, 71, 3507014(2022).

    [11] 周涛, 杜玉虎, 石道宗, 等. 强化特征提取能力的下颌骨骨折检测3M-YOLOv5网络[J]. 光学 精密工程, 2023, 31(21): 3178-3191. doi: 10.37188/ope.20233121.3178ZHOUT, DUY H, SHID Z, et al. Mandibular fracture detection with 3M-YOLOv5 network based on enhanced feature extraction capability[J]. Opt. Precision Eng., 2023, 31(21): 3178-3191.(in Chinese). doi: 10.37188/ope.20233121.3178

    [12] K HAN, Y H WANG, Q TIAN et al. GhostNet: more features from cheap operations, 1577-1586(2020).

    [13] C WANG, X BAI, S WANG et al. Multiscale visual attention networks for object detection in VHR remote sensing images. IEEE Geoscience and Remote Sensing Letters, 16, 310-314(2019).

    [14] J L WANG, C Q XU, Z L YANG et al. Deformable convolutional networks for efficient mixed-type wafer defect pattern recognition. IEEE Transactions on Semiconductor Manufacturing, 33, 587-596(2020).

    [15] J F DAI, H Z QI, Y W XIONG et al. Deformable convolutional networks, 764-773(2017).

    [16] 刘彦磊, 李孟喆, 王宣宣. 轻量型YOLOv5s车载红外图像目标检测[J]. 中国光学(中英文), 2023(5): 1045-1055. doi: 10.37188/co.2022-0254LIUY L, LIM Z, WANGX X. Lightweight YOLOv5s vehicle infrared image target detection[J]. Chinese Optics, 2023(5): 1045-1055.(in Chinese). doi: 10.37188/co.2022-0254

    [17] Q B HOU, D Q ZHOU, J S FENG. Coordinate attention for efficient mobile network design, 13708-13717(2021).

    [18] J TERVEN, D CORDOVA-ESPARZA. A comprehensive review of YOLO architectures in computer vision: from YOLOv1 to YOLOv8 and YOLO-NAS. arXiv, 2304-00501(2023). http://arxiv.org/abs/2304.00501

    [19] 周启宸, 王伯超. 基于改进YOLOv7的太阳能电池片表面缺陷检测[J]. 计算机应用, 2023, 43(S02): 223-228.ZHOUQ C, WANGB C. Solar cell surface defect detection based on improved YOLOv7[J]. Journal of Computer Applications, 2023, 43(S02): 223-228.(in Chinese)

    [20] M X TAN, R M PANG, Q V LE. EfficientDet: scalable and efficient object detection, 10778-10787(2020).

    [21] W LIU, D ANGUELOV, D ERHAN et al. SSD: single shot MultiBox detector, 21-37(2016).

    Ying ZHOU, Shibo XU, Haiyong CHEN, Kun LIU. Solar cell defect detection network combining multiscale feature and attention[J]. Optics and Precision Engineering, 2024, 32(14): 2286
    Download Citation