• Laser & Optoelectronics Progress
  • Vol. 62, Issue 2, 0212007 (2025)
Guangzhi Zhang*, Huimin Li, and Xuning Song
Author Affiliations
  • College of Mechanical Engineering, Donghua University, Shanghai 201620, China
  • show less
    DOI: 10.3788/LOP240983 Cite this Article Set citation alerts
    Guangzhi Zhang, Huimin Li, Xuning Song. Defect Detection of Tubular Containers Based on an Unsupervised Domain Adaptive Algorithm[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0212007 Copy Citation Text show less
    References

    [1] Huang J J. Research on the online labeling system based on machine the vision[D], 1-5(2014).

    [2] Yu J J, Zhou J P, Xue R L et al. Weld surface quality detection based on structured light and illumination model[J]. Chinese Journal of Lasers, 49, 1602019(2022).

    [3] Yang Z Q, Zhang M X, Chen Y S et al. Review of surface defect detection methods based on machine vision[J]. Modern Manufacturing Engineering, 143-156(2023).

    [4] Bhatt P M, Malhan R K, Rajendran P et al. Image-based surface defect detection using deep learning: a review[J]. Journal of Computing and Information Science in Engineering, 21, 040801(2021).

    [5] Cheng J F, Fang G S, Gao H F. Research progress of surface defect detection based on machine vision technology[J]. Application Research of Computers, 40, 967-977(2023).

    [6] Li J, Li H, Hu X K et al. Research progress of surface defect detection technology based on deep learning[J]. Computer Integrated Manufacturing Systems, 30, 774-790(2024).

    [7] Cheng S, Yang H G, Xu X Q et al. Improved lightweight X-ray aluminum alloy weld defects detection algorithm based on YOLOv5[J]. Chinese Journal of Lasers, 49, 2104005(2022).

    [8] Liao L F, Li S K, Wang X C. Lithography hotspot detection method based on pre-trained VGG11 model[J]. Acta Optica Sinica, 43, 0312008(2023).

    [9] Li X, Li X R, Wang C et al. Aero-engine surface defect detection model based on improved YOLOv5[J]. Laser & Optoelectronics Progress, 60, 1615007(2023).

    [10] Tan C, Sun F, Kong T et al. A survey on deep transfer learning[M]. Artificial neural networks and machine learning-ICANN 2018, 11141, 270-279(2018).

    [11] Hao B Q, Fan Y G, Song Z H. Deep transfer learning-based pulsed eddy current thermography for crack defect detection[J]. Acta Optica Sinica, 43, 0415002(2023).

    [12] Wang M, Deng W H. Deep visual domain adaptation: a survey[J]. Neurocomputing, 312, 135-153(2018).

    [13] Yang X L, Song Z X, King I et al. A survey on deep semi-supervised learning[J]. IEEE Transactions on Knowledge and Data Engineering, 35, 8934-8954(2023).

    [14] Oza P, Sindagi V A, Vs V et al. Unsupervised domain adaptation of object detectors: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 4018-4040(2024).

    [15] Sun Q Y, Zhao C Q, Ju Y et al. A survey on unsupervised domain adaptation in computer vision tasks[J]. Scientia Sinica (Technologica), 52, 26-54(2022).

    [16] Chen Y H, Li W, Sakaridis C et al. Domain adaptive faster R-CNN for object detection in the wild[C], 3339-3348(2018).

    [17] Hnewa M, Radha H. Multiscale domain adaptive yolo for cross-domain object detection[C], 3323-3327(2021).

    [18] Zhang S Z, Tuo H Y, Hu J et al. Domain adaptive YOLO for one-stage cross-domain detection[EB/OL]. https://arxiv.org/abs/2106.13939

    [19] Feng T R, Miao Y B, Zhao S. Defect detection of cosmetic plastic bottles based on deep learning[J]. Journal of Donghua University (Natural Science), 46, 269-274(2020).

    [20] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C], 580-587(2014).

    [21] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

    [22] Long M S, Cao Y, Wang J M et al. Learning transferable features with deep adaptation networks[EB/OL]. https://arxiv.org/abs/1502.02791

    [23] Ganin Y, Ustinova E, Ajakan H et al. Domain-adversarial training of neural networks[M]. Domain adaptation in computer vision applications, 189-209(2017).

    Guangzhi Zhang, Huimin Li, Xuning Song. Defect Detection of Tubular Containers Based on an Unsupervised Domain Adaptive Algorithm[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0212007
    Download Citation