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SSD-based carton packaging quality defect detection system for the logistics supply chain

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
With the deepening of green and sustainable development and the rapid development of the social economy, the modern logistics industry has also developed to an unprecedented level. In the logistics supply chain, due to the high value of the items inside the arrival carton, appearance inspection must be carried out before warehousing. However, manual inspection is slow and ineffective, resulting in the waste of manpower and packaging carton resources, which is not conducive to sustainable development. To address the above problems, this paper designs a logistics supply chain carton packaging quality defect detection system based on improved Single Shot MultiBox Detector (SSD) in the context of green sustainable development. The Implicit Feature Pyramid Network (IFPN) is introduced into SSD to improve the feature extraction ability of the model; the multiscale attention mechanism is introduced to collect more feature information. The experiment shows that the mAP and FPS of the system on the self-built data set reach 0.9662 and 36 respectively, which can realise the detection of the appearance defects of logistics cartons and help promote green sustainable development.
Rocznik
Strony
117--123
Opis fizyczny
Bibliogr. 16 poz., tab.
Twórcy
autor
  • Yiwu Industrial & Commercial College, Yiwu 322000, Zhejiang, China
autor
  • iujiang University, Jiujiang 332005, Jiangxi, China
autor
  • Zhejiang Institute of Mechanical and Electrical Technician, Yiwu 322000, Zhejiang, China
Bibliografia
  • [1] Yang X, Han MR, Tan HL, Li Q, Luo X. Detecting defects with support vector machine in logistics packaging boxes for edge computing. IEEE Access. 2020;8:64002-10. DOI: 10.1109/access.2020.2984539.
  • [2] Xu XY, Yang Y. Analysis of the dilemma of promoting circular logistics packaging in China: A stochastic evolutionary game-based approach. Int J Environ Res Public Health. 2022;19(12):7363. DOI: 10.3390/IJERPH19127363.
  • [3] Zhang YJ, Xie YQ. Adaptive clustering feature matching algorithm based on SIFT and RANSAC. Proc 2nd Int Conf Electronics, Communications Information Technol. (CECIT 2021). 2021:210-5. DOI: 10.26914/c.cnkihy.2021.065415.
  • [4] Zhou W, Luo SY. Pedestrian detection with improved LBP and hog algorithm. Open Access Library J. 2018;5(4):1-10. DOI: 10.4236/oalib.1104573.
  • [5] Chen XH. E-commerce logistics inspection system based on artificial intelligence technology in the context of big data. Security and Communication Networks. Special Issue. 2022:3418466. DOI: 10.1155/2022/3418466.
  • [6] Zhu HJ, Wang YC, Fan JW. IA-Mask R-CNN: Improved anchor design mask R-CNN for surface defect detection of automotive engine parts. Appl Sci. 2022;12(13):6633. DOI: 10.3390/APP12136633.
  • [7] Liu YT, Wang YM. Synthetic aperture radar image target recognition based on improved fusion of R-FCN and SRC. Proc 4th Int Conf Computer Sci Software Eng (CSSE 2021). 2021:64-71. DOI: 10.26914/c.cnkihy.2021.052077.
  • [8] Yao J, Wang ZQ, Liu CH, Huang GC, Yuan QB, Xu K, et al. Detection method of crushing mouth loose material blockage based on SSD algorithm. Sustainability. 2022;14(21):14386. DOI: 10.3390/SU142114386.
  • [9] Jung H, Rhee J. Application of YOLO and ResNet in heat staking process inspection. Sustainability. 2022;14(23):15892. DOI: 10.3390/SU142315892.
  • [10] Lin TY, Dollar P, Girshick R, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. 2017 IEEE Conf Computer Vision Pattern Recognition (CVPR). Honolulu, HI, USA. 2017:936-44. DOI: 10.1109/CVPR.2017.106.
  • [11] Singh B, Davis LS. An analysis of scale invariance in object detection-SNIP. 2018 IEEE/CVF Conf Computer Vision Pattern Recognition. Salt Lake City, UT, USA. 2018:3578-87. DOI: 10.48550/arXiv.1711.08189.
  • [12] Liu S, Qi L, Qin HF, Shi JP, Jia JY. Path aggregation network for instance segmentation. 2018 IEEE/CVF Conf Computer Vision Pattern Recognition. Salt Lake City, UT, USA. 2018:8759-68. DOI: 10.1109/CVPR.2018.00913.
  • [13] Li R, Wang LB, Zhang C, Duan CX, Zheng SY. A2-FPN for semantic segmentation of fine-resolution remotely sensed images. Int J Remote Sensing. 2022;43(3):1131-55. DOI: 10.1080/01431161.2022.2030071.
  • [14] Cheng L, Ji YC, Li C, Liu XJ, Fang GY. Improved SSD network for fast concealed object detection and recognition in passive terahertz security images. Sci Rep. 2022;12(1):12082. DOI: 10.1038/S41598-022-16208-0.
  • [15] Wu CH. An empirical study on discussion and evaluation of green university. Ecol Chem Eng S. 2021; 28(1):75-87. DOI: 10.2478/eces-2021-0007.
  • [16] Wu CH, Tsai SB, Liu W, Shao XF, Sun R, Wacławek M. Eco-technology and eco-innovation for green sustainable growth. Ecol Chem Eng S. 2021;28(1):7-10. DOI: 10.2478/eces-2021-0001.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a17b1b00-59ad-453e-8e66-81fd15d0884e
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