Journal of Qufu Normal University, Volume. 51, Issue 3, 74(2025)

The commodity recognition algorithm based on YOLO-DA

TIAN Haifeng*, QIU Maoshun, and ZHANG Weijian
Author Affiliations
  • School of Cyber Science and Engineering, Qufu Normal University, 273165, Qufu, Shandong, PRC
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    References(14)

    [1] [1] KOUBAROULIS D, MATAS J. Evaluating colour-based object recognition algorithms using the SOIL-47 database[C]//Asia Conference on Computer Vision, 2002(2):2.

    [2] [2] GEORGE M, FLOERKEMEIER C. Recognizing products:A per-exemplar multi-label image classification approach[C]//ECCV 2014(Lecture Notes in Computer Science). Cham:Springer, 2014:440-445.

    [3] [3] WEI X S, CUI Q, YANG L, et al. RPC:A large-scale retail product checkout dataset [EB/OL].https://arxiv.org/abs/1901.07249.

    [4] [4] LI C C, DU D W, ZHANG L B, et al. Data priming network for automatic check-out [C]//Proceedings of the 27th ACM International Conference on Multimedia, 2019:2152-2160.

    [5] [5] HSU C C, TSAI Y H, LIN Y Y, et al. Every pixel matters:Center-aware feature alignment for domain adaptive object detector[C]//16th European Conference on Computer Vision. Springer International Publishing, 2020:733-748.

    [6] [6] TIAN Z, SHEN C, CHEN H, et al. FCOS:Fully convolutional one-stage object detection[J]. Advances in Neural Information Processing Systems, 2022(35):34899-34911.

    [7] [7] ZHOU H, JIANG F, LU H. SSDA-YOLO:Semi-supervised domain adaptive YOLO for cross-domain object detection[J]. Computer Vision and Image Understanding, 2023(229):103649.

    [8] [8] REDMON J, ALI F. YOLO9000:Better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017:6517-6525.

    [9] [9] ZHANG H Y, WANG Y, DAYOUB F, et al. Varifocal-Net:An IoU-aware dense object detector [C]//2021 IEEE/CVF Conference on CVPR, IEEE, 2021 :8510-8519.

    [10] [10] GEVORGYAN Z. SIoU loss:More powerful learning for bounding box regression [EB/OL].https://arxiv.org/abs/2205.12740.

    [11] [11] LI C Y, LI L L, JIANG H L, et al. YOLOv6:A singlestage object detection framework for industrial applicatios[EB/OL].https://arxiv.org/abs/2209.02976.

    [12] [12] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7:Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2023:7464-7475.

    [13] [13] TAN M X, PANG R M, LE Q V. EfficientDet:Scalable and efficient object detection[C]//IEEE/CVF Conference on CVPR, 2020:10778-10787.

    [14] [14] GE Z, LIU S T, WANG F, et al. YOLOX:Exceeding YOLO series in 2021 [EB/OL].https://arxiv.org/abs/2107.08430v1.

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    TIAN Haifeng, QIU Maoshun, ZHANG Weijian. The commodity recognition algorithm based on YOLO-DA[J]. Journal of Qufu Normal University, 2025, 51(3): 74

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    Paper Information

    Received: Dec. 18, 2023

    Accepted: Aug. 25, 2025

    Published Online: Aug. 25, 2025

    The Author Email: TIAN Haifeng (wmthf@163.com)

    DOI:10.3969/j.issn.1001-5337.2025.3.074

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