[en] In this study, we apply concepts taken from the fields of Artificial Intelligence (AI) and Industry 4.0 to a belt conveyor, a key tool in the packaging and logistics industries. Specifically, we present an item classification model built for belt conveyors, helping the conveyor control system to recognize items while minimizing its impact on the conveyor design and the movement of items. To that end, we followed a three-pronged approach. First, we converted a size measurement system into a 3-D shape reconstruction system by recycling a belt conveyor prototype developed in a previous study. Secondly, we transformed a scanned point cloud that varies in size, given the use of variable-length items, into a point cloud with a fixed size. Thirdly, we constructed three different end-to-end 3-D point cloud classification models, with the Dynamic Graph Convolutional Neural Network (DGCNN) model coming out on top when considering accuracy, response time, and training stability.
Disciplines :
Computer science
Author, co-author :
Park, Ho-Min
Kang, Byungkon
Van Messem, Arnout ; Université de Liège - ULiège > Département de mathématique > Statistique applquée aux sciences
De Neve, Wesley
Language :
English
Title :
3-D Deep Learning-Based Item Classification for Belt Conveyors Targeting Packaging and Logistics
Publication date :
2021
Event name :
25th International Conference on Pattern Recognition: 1st International Workshop on Industrial Machine Learning
Event date :
10-15 January 2021
Audience :
International
Main work title :
Pattern Recognition. ICPR International Workshops and Challenges
Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., Yin, B.: Smart factory of industry 4.0: key technologies, application case, and challenges. IEEE Access 6, 6505–6519 (2018)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT press Cambridge (2016)
Gronau, N., Grum, M., Bender, B.: Determining the optimal level of autonomy in cyber-physical production systems. In: 2016 IEEE 14th International Conference on Industrial Informatics (INDIN), pp. 1293–1299 (2016)
Hermann, M., Pentek, T., Otto, B.: Design Principles for Industrie 4.0 scenarios. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), pp. 3928–3937 (2016)
International Electrotechnical Commission: IEC 61131–9: IEC 61131–9:2013 Programmable controllers-Part 9: Single-drop digital communication interface for small sensors and actuators (SDCI) (2013). https://webstore.iec.ch/publication/4558. Accessed 21 Sept 2020
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence (1995)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, pp. 1097–1105. Curran Associates, Inc. (2012)
Kujala, J.V., Lukka, T.J., Holopainen, H.: Classifying and sorting cluttered piles of unknown objects with robots: a learning approach. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 971–978. IEEE (2016)
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989).https://doi.org/10.1162/neco.1989.1.4.541
Lu, H., Shi, H.: Deep learning for 3D point cloud understanding: a survey. arXiv preprint arXiv:2009.08920 (2020)
Mezei, A., Tamás, L., Buşoniu, L.: Sorting objects from a conveyor belt using active perception with a POMDP model. In: 2019 18th European Control Conference (ECC), pp. 2466–2471. IEEE (2019)
Mohamed, M.: Challenges and benefits of industry 4.0: an overview. Int. J. Supply Oper. Manage. 5(3), 256–265 (2018). https://doi.org/10.22034/2018.3.7, http://www.ijsom.com/article 2767.html
Murphy, K.P.: Machine Learning: A Probabilistic Perspective, pp. 1–2. MIT press, Cambridge (2012)
Park, H., Van Messem, A., De Neve, W.: Item measurement for logistics-oriented belt conveyor systems using a scenario-driven approach and automata-based control design. In: 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA), pp. 271–280 (2020)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 652–660, July 2017
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 5099–5108. Curran Associates, Inc. (2017)
Rojko, A.: Industry 4.0 concept: background and overview. Int. J. Interact. Mob. Technol. (iJIM) 11(5), 77–90 (2017). https://online-journals.org/index.php/i-jim/article/view/7072
Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)
Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 945–953, December 2015
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 270–279. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7 27
Visa, S., Ramsay, B., Ralescu, A.L., Van Der Knaap, E.: Confusion Matrix-based Feature Selection. vol. 710, pp. 120–127 (2011). https://openworks.wooster.edu/facpub/88
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. 38(5), October 2019. https://doi.org/10.1145/3326362
Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912–1920, June 2015
Zhang, Y., Li, L., Ripperger, M., Nicho, J., Veeraraghavan, M., Fumagalli, A.: Gilbreth: a conveyor-belt based pick-and-sort industrial robotics application. In: 2018 Second IEEE International Conference on Robotic Computing (IRC), pp. 17–24. IEEE (2018)
Zhou, Y., Tuzel, O.: VoxelNet: end-to-end learning for point cloud based 3D object detection. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4490–4499 (2018). https://doi.org/10.1109/CVPR.2018.00472
