Abstract
The increasing consumption of electrical and electronic devices is alarming. Therefore, the transition from linear to circular economy becomes essential. The key solution to support this transformation is artificial intelligence. This work presents a transfer learning approach to support the recycling of electrical and electronic waste (Ewaste). We emphasize the use of transfer learning technique, particularly, to classify Ewaste. In this approach, we design a hybrid model of residual nets and inception modules that can classify features of a source domain (smartphones in our case) and leverage this knowledge to another device (electric screwdrivers, as an example). Using our model, we achieve an overall accuracy of \(94.27\%\) and \(97.22\%\), respectively. These are comparable to the popular pre-trained models, which use similar network topologies. We use a web crawler program for collecting images from search engines to build the datasets with less efforts. We show that transfer learning is more robust and performs better than training from scratch. It avoids duplication and waste of computational resources. As a result, with the benefits of transfer learning, we can provide detailed information about the devices that need to be recycled. Ultimately, this would greatly support the overall recycling process.
This work has been funded by the Ministry of Economy, Innovation, Digitization, and Energy of the State of North Rhine-Westphalia within the project Prosperkolleg.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nordmann, J., Oettershagen: Factsheets zum Thema Mobiltelefone und Nachhaltigkeit December 2013. https://wupperinst.org/uploads/tx_wupperinst/mobiltelefone_factsheets.pdf
Abou Baker, N., Szabo-Müller, P., Handmann, U.: A feature-fusion transfer learning method as a basis to support automated smartphone recycling in a circular smart city. In: Paiva, S., Lopes, S.I., Zitouni, R., Gupta, N., Lopes, S.F., Yonezawa, T. (eds.) SmartCity360\(^{\circ }\) 2020. LNICST, vol. 372, pp. 422–441. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-76063-2_29
Abou Baker, N., Szabo-Müller, P., Handmann, U.: Transfer learning-based method for automated e-waste recycling in smart cities. EAI Endors. Trans. Smart Cities 5(16) (2021). https://doi.org/10.4108/eai.16-4-2021.169337
Abou Baker, N., Zengeler, N., Handmann, U.: A transfer learning evaluation of deep neural networks for image classification. Mach. Learn. Knowl. Extract. 4(1), 22–41 (2022). https://doi.org/10.3390/make4010002, https://www.mdpi.com/2504-4990/4/1/2
Ari, V.: A review of technology of metal recovery from electronic waste. E-Waste in transition–from pollution to resource (2016)
Chakraborty, H., Samanta, P., Zhao, L.: Sequential data imputation with evolving generative adversarial networks. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2021). https://doi.org/10.1109/IJCNN52387.2021.9534108
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
Hwang, K.H., Lee, M.J., Ha, Y.G.: A befitting image data crawling and annotating system with CNN based transfer learning. In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 165–168 (2020). https://doi.org/10.1109/BigComp48618.2020.00-81
Koniusz, P., Tas, Y., Porikli, F.: Domain adaptation by mixture of alignments of second- or higher-order scatter tensors (2017)
Kornblith, S., Shlens, J., Le, Q.V.: Do better ImageNet models transfer better? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Kuo, C.W., Ashmore, J., Huggins, D., Kira, Z.: Data-efficient graph embedding learning for PCB component detection (2018)
di Liao, Y.: A web-based dataset for garbage classification based on Shanghai’s rule. Int. J. Mach. Learn. Comput. 10, 599–604 (2020)
Liu, Z., Sabar, N., Song, A.: Partial transfer learning for fast evolutionary generative adversarial networks. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2021). https://doi.org/10.1109/IJCNN52387.2021.9533384
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Unsupervised domain adaptation with residual transfer networks (2017)
Maurice, A.A., Dinh, K.N., Charpentier, N.M., Brambilla, A., Gabriel, J.C.P.: Dismantling of printed circuit boards enabling electronic components sorting and their subsequent treatment open improved elemental sustainability opportunities. Sustainability 13(18) (2021). https://doi.org/10.3390/su131810357, https://www.mdpi.com/2071-1050/13/18/10357
Neirotti, P.: Adapting market proposition of a waste management system to customers’ needs. Master’s thesis, Politecnico di Torino (2021). https://webthesis.biblio.polito.it/18449/
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010). https://doi.org/10.1109/TKDE.2009.191
Sarc, R.: The “rewaste4.0” project–a review. Processes 9(5) (2021). https://www.mdpi.com/2227-9717/9/5/764
Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015). https://doi.org/10.1109/CVPR.2015.7298594
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016). https://doi.org/10.1109/CVPR.2016.308
Tiwari, M., Sanodiya, R.K., Mathew, J., Saha, S.: Multi-source based approach for visual domain adaptation. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2021). https://doi.org/10.1109/IJCNN52387.2021.9534305
Wilts, H., Garcia, B.R., Garlito, R.G., Gómez, L.S., Prieto, E.G.: Artificial intelligence in the sorting of municipal waste as an enabler of the circular economy. Resources 10(4) (2021). https://doi.org/10.3390/resources10040028, https://www.mdpi.com/2079-9276/10/4/28
Yang, Z., Cao, S.: Job information crawling, visualization and clustering of job search websites. In: 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), vol. 1, pp. 637–641 (2019). https://doi.org/10.1109/IAEAC47372.2019.8997713
Yi, C., Wang, J., Cheng, N., Zhou, S., Xu, B.: Transfer ability of monolingual wav2vec2.0 for low-resource speech recognition. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–6 (2021). https://doi.org/10.1109/IJCNN52387.2021.9533587
Zellinger, W., Grubinger, T., Lughofer, E., Natschläger, T., Saminger-Platz, S.: Central moment discrepancy (CMD) for domain-invariant representation learning (2019)
Zhao, L., Kong, W., Wang, C.: Electricity corpus construction based on data mining and machine learning algorithm. In: 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), pp. 1478–1481 (2020). https://doi.org/10.1109/ITOEC49072.2020.9141559
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Abou Baker, N., Stehr, J., Handmann, U. (2022). Transfer Learning Approach Towards a Smarter Recycling. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13529. Springer, Cham. https://doi.org/10.1007/978-3-031-15919-0_57
Download citation
DOI: https://doi.org/10.1007/978-3-031-15919-0_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15918-3
Online ISBN: 978-3-031-15919-0
eBook Packages: Computer ScienceComputer Science (R0)