Image based classification of shipments using transfer learning

• Autorzy: Leppioja Markus , Luukka Pasi , Lohrmann Christoph

Identyfikatory

DOI

10.15439/2021B4

• Konferencja: International Scientific Conference Knowledge on Economics and Management KNOWCON 2021, (17 ; 11-12.11.2021 ; Olomuniec, Czechy)
• Języki publikacji: EN

Abstrakty

EN

This paper focuses on recognizing different postal shipment types from images taken by the sorting machine. Greyscale images obtained from sorting machines are used to build a classifier using transfer learning to recognize seven different classes of shipments. Three convolutional neural networks (VGG16, GoogLeNet and ResNet50), that were pretrained using the ImageNet dataset, were used as feature extractors and the extracted features were subsequently supplied to a neural network classifier. VGG16 demonstrated the best performance for six out of the seven classes and achieved an overall mean accuracy of 95.69% on the independent test set. The model accomplished F1 scores exceeding 90% for five out of seven classes, only having a lower recall for the aggregated class "Other'' and shipments from abroad. The results of this study highlight the potential of transfer learning for computer vision in the context of shipment classification.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 29
• Strony: 37--44
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Leppioja Markus

• Loihde Analytics Valtakatu 49, 53100 Lappeenranta, Finland

autor

Luukka Pasi

• School of Business & Management Yliopistonkatu 34, 53850 Lappeenranta, Finland

autor

Lohrmann Christoph

• School of Business & Management Yliopistonkatu 34, 53850 Lappeenranta, Finland

Bibliografia

• 1. C. C. Aggarwal, Neural Networks and Deep Learning: A Textbook. Cham: Springer International Publishing, 2018.
• 2. B. Pardamean, T. W. Cenggoro, R. Rahutomo, A. Budiarto, and E. K. Karuppiah, “Transfer Learning from Chest X-Ray Pre-trained Convolutional Neural Network for Learning Mammogram Data,” Procedia Comput. Sci., vol. 135, pp. 400–407, 2018, http://dx.doi.org/https://doi.org/10.1016/j.procs.2018.08.190.
• 3. X. Sun and H. Qian, “Chinese Herbal Medicine Image Recognition and Retrieval by Convolutional Neural Network,” PLoS One, vol. 11, no. 6, pp. 1–19, 2016, http://dx.doi.org/10.1371/journal.pone.0156327.
• 4. A. S. B. Reddy and D. S. Juliet, “Transfer Learning with ResNet-50 for Malaria Cell-Image Classification,” in 2019 International Conference on Communication and Signal Processing (ICCSP), 2019, pp. 945–949, http://dx.doi.org/10.1109/ICCSP.2019.8697909.
• 5. J. Chmielinska and J. Jakubowski, “Detection of driver fatigue symptoms using transfer learning,” Bull. Polish Acad. Sci., vol. 66, no. 6, pp. 869–874, 2018, http://dx.doi.org/10.24425/bpas.2018.125934.
• 6. A. Abu Mallouh, Z. Qawaqneh, and B. D. Barkana, “Utilizing CNNs and transfer learning of pre-trained models for age range classification from unconstrained face images,” Image Vis. Comput., vol. 88, pp. 41–51, 2019, http://dx.doi.org/https://doi.org/10.1016/j.imavis.2019.05.001.
• 7. M. Sert and E. Boyaci, “Sketch Recognition Using Transfer Learning,” Multimed. Tools Appl., vol. 78, no. 12, pp. 17095–17112, 2019, http://dx.doi.org/10.1007/s11042-018-7067-1.
• 8. P. Sangkloy, N. Burnell, C. Ham, and J. Hays, “The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies,” ACM Trans. Graph., vol. 35, no. 4, 2016, http://dx.doi.org/10.1145/2897824.2925954.
• 9. Y. Fu and C. Aldrich, “Froth image analysis by use of transfer learning and convolutional neural networks,” Miner. Eng., vol. 115, pp. 68–78, 2018, http://dx.doi.org/https://doi.org/10.1016/j.mineng.2017.10.005.
• 10. S. Shao, S. McAleer, R. Yan, and P. Baldi, “Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning,” IEEE Trans. Ind. Informatics, vol. 15, no. 4, pp. 2446–2455, 2019, http://dx.doi.org/10.1109/TII.2018.2864759.
• 11. M. Mehdipour Ghazi, B. Yanikoglu, and E. Aptoula, “Plant identification using deep neural networks via optimization of transfer learning parameters,” Neurocomputing, vol. 235, pp. 228–235, 2017, http://dx.doi.org/https://doi.org/10.1016/j.neucom.2017.01.018.
• 12. D. Shustrov, T. Eerola, L. Lensu, H. Kälviäinen, and H. Haario, “Fine-Grained Wood Species Identification Using Convolutional Neural Networks,” in Image Analysis, 2019, pp. 67–77.
• 13. T. O. Camargo et al., “Detecting a predefined solar spot group with a pretrained convolutional neural network,” in 2019 IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI), 2019, pp. 1–6, http://dx.doi.org/10.1109/ColCACI.2019.8781990.
• 14. B. Zhao, B. Huang, and Y. Zhong, “Transfer Learning With Fully Pretrained Deep Convolution Networks for Land-Use Classification,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 9, pp. 1436–1440, 2017, http://dx.doi.org/10.1109/LGRS.2017.2691013.
• 15. C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer ScienceBusiness Media, 2006.
• 16. H. H. Aghdam and E. J. Heravi, Guide to convolutional neural networks. A practical application to traffic-sign detection and classification. Springer International Publishing, 2017.
• 17. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, 1998, http://dx.doi.org/10.1109/5.726791.
• 18. G. Rebala, A. Ravi, and S. Churiwala, An introduction to machine learning. Springer International Publishing, 2019.
• 19. M. Salvaris, D. Dean, and W. H. Tok, Deep learning with Azure. Building and deploying artificial intelligence solutions on the Microsoft AI platform. Apress, 2018.
• 20. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255, http://dx.doi.org/10.1109/CVPR.2009.5206848.
• 21. ImageNet, “Summary and Statistics,” 2020. http://image-net.org/about-stats (accessed Feb. 29, 2020).
• 22. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in 3rd International Conference on Learning Representations, (ICLR), 2015, pp. 1–14, [Online]. Available: http://arxiv.org/abs/1409.1556.
• 23. O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015, http://dx.doi.org/10.1007/s11263-015-0816-y.
• 24. C. Szegedy et al., “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–9, http://dx.doi.org/10.1109/CVPR.2015.7298594.
• 25. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778, doi: 10.1109/CVPR.2016.90.
• 26. S. Pattanayak, Pro deep learning with TensorFlow. A mathematical approach to advanced artificial intelligence in Python. Apress, 2017.
• 27. L. Mou and Z. Jin, Tree-Based Convolutional Neural Networks: Principles and Applications, 1st ed. Springer Publishing Company, Incorporated, 2018.
• 28. M. Leppioja, Shipment type classification from images, Master's thesis, LUT University, 2020
• 29. K. Danilchenko and M. Segal, An efficient connected swarm deployment via deep learning, Annals of Computer Science and Information Systems, 25, 2021, pp. 1-7.
• 30. A. M. Nguyen and H.S. Nguyen, Rotation Invariance in Graph Convolutional Networks, Annals of Computer Science and Information Systems, 25, 2021, pp. 81-90.