The Development of Real-Time Mobile Garbage Detection Using Deep Learning

Authors

  • Haris Imam Karim Fathurrahman Ahmad Dahlan University
  • Alfian Ma'arif Ahmad Dahlan University
  • Li-Yi Chin National Yunlin University of Science and Technology

DOI:

https://doi.org/10.26555/jiteki.v7i3.22295

Keywords:

Database, Deep learning, Garbage detection, Mobile application.

Abstract

The problem of garbage in the world is a serious issue that must be solved. Good garbage management is a must for now and in the future. Good garbage management is accompanied by a system of classification and sorting of garbage types. This study aims to create a mobile-based application that can select the type of garbage and enter the garbage data into a database. The database used is a Google SpreadSheet that will accommodate data from the output issued by the garbage detection mobile application. The image data used in this study amounted to 10108 images and was divided into six different garbage classes. This study uses a deep learning platform called densenet121 with an accuracy rate of 99.6% to train the image data. DenseNet121 has been modified and added an optimization based on a genetic algorithm. The genetic algorithm applied in the optimization uses four generations. The model resulting from the training of the two approaches is converted into a model that mobile applications can access. The mobile application based on a deep learning model accommodates the detection data of the type of garbage, the level of detection accuracy, and the GPS location of the garbage. In the final experiment of the mobile application, the delay time in sending data was very fast, which was less than one second (0.86s).

References

C. Ellis, “World Bank: Global waste generation could increase 70% by 2050,†WasteDive, 2018. https://www.wastedive.com/news/world-bank-global-waste-generation-2050/533031/ (accessed Nov. 17, 2021).

S. Sambaraju and V. Sree Lakshmi, “Eco-friendly treatment of dairy wastewater using garbage enzyme,†Mater. Today Proc., vol. 33, pp. 650–653, 2020. https://doi.org/10.1016/j.matpr.2020.05.719

S. Negi, A. Mandpe, A. Hussain, and S. Kumar, “Collegial effect of maggots larvae and garbage enzyme in rapid composting of food waste with wheat straw or biomass waste,†J. Clean. Prod., vol. 258, p. 120854, Jun. 2020. https://doi.org/10.1016/j.jclepro.2020.120854

W.-L. Mao, W.-C. Chen, C.-T. Wang, and Y.-H. Lin, “Recycling waste classification using optimized convolutional neural network,†Resour. Conserv. Recycl., vol. 164, p. 105132, 2021. https://doi.org/10.1016/j.resconrec.2020.105132

M. Toğaçar, B. Ergen, and Z. Cömert, “Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models,†Measurement, vol. 153, p. 107459, Mar. 2020. https://doi.org/10.1016/j.measurement.2019.107459

O. Adedeji and Z. Wang, “Intelligent waste classification system using deep learning convolutional neural network,†Procedia Manuf., vol. 35, pp. 607–612, 2019. https://doi.org/10.1016/j.promfg.2019.05.086

V. Punitha and C. Mala, “Traffic classification for connectionless services with incremental learning,†Comput. Commun., vol. 150, pp. 185–199, Jan. 2020. https://doi.org/10.1016/j.comcom.2019.11.017

C. Zhang, X. Zhang, D. Tu, and Y. Wang, “Small object detection using deep convolutional networks: applied to garbage detection system,†J. Electron. Imaging, vol. 30, no. 04, Aug. 2021. https://doi.org/10.1117/1.JEI.30.4.043013

Z. Yang and J. Chang, “A multi-attribute decision-making-based site selection assessment algorithm for garbage disposal plant using interval q-rung orthopair fuzzy power Muirhead mean operator,†Environ. Res., vol. 193, p. 110385, Feb. 2021. https://doi.org/10.1016/j.envres.2020.110385

W. Chen, H. Wang, H. Li, Q. Li, Y. Yang, and K. Yang, “Real-Time Garbage Object Detection With Data Augmentation and Feature Fusion Using SUAV Low-Altitude Remote Sensing Images,†IEEE Geosci. Remote Sens. Lett., pp. 1–5, 2021. https://doi.org/10.1109/LGRS.2021.3074415

M. U. Sohag and A. K. Podder, “Smart garbage management system for a sustainable urban life: An IoT based application,†Internet of Things, vol. 11, p. 100255, Sep. 2020. https://doi.org/10.1016/j.iot.2020.100255

D. Abuga and N. . Raghava, “Real-time smart garbage bin mechanism for solid waste management in smart cities,†Sustain. Cities Soc., vol. 75, p. 103347, Dec. 2021. https://doi.org/10.1016/j.scs.2021.103347

C. Wang, J. Qin, C. Qu, X. Ran, C. Liu, and B. Chen, “A smart municipal waste management system based on deep-learning and Internet of Things,†Waste Manag., vol. 135, no. August, pp. 20–29, 2021. https://doi.org/10.1016/j.wasman.2021.08.028

T. Urbanczyk and L. Peter, “Database Development for the Urgent Department of Hospital based on Tagged Entity Storage Following the IoT Concept,†IFAC-PapersOnLine, vol. 49, no. 25, pp. 278–283, 2016. https://doi.org/10.1016/j.ifacol.2016.12.047

C.-Y. Huang and Y.-J. Chang, “An adaptively multi-attribute index framework for big IoT data,†Comput. Geosci., vol. 155, p. 104841, Oct. 2021. https://doi.org/10.1016/j.cageo.2021.104841

Y. Chen, J. Nakazawa, T. Yonezawa, and H. Tokuda, “Cruisers: An automotive sensing platform for smart cities using door-to-door garbage collecting trucks,†Ad Hoc Networks, vol. 85, pp. 32–45, Mar. 2019. https://doi.org/10.1016/j.adhoc.2018.10.018

R. Abishega, S. Saranya, M. Devayani, C. P. Subiksha, and G. Sudha, “Driverless vehicle for garbage collection,†Mater. Today Proc., Mar. 2021. https://doi.org/10.1016/j.matpr.2021.01.044

S. Parasuraman, A. Sam, S. K. Yee, B. C. Chuon, and L. Ren, “Smartphone usage and increased risk of mobile phone addiction: A concurrent study,†Int. J. Pharm. Investig., vol. 7, no. 3, p. 125, 2017. https://doi.org/10.4103/jphi.JPHI_56_17

S. Köse and M. Murat, “Examination of the relationship between smartphone addiction and cyberchondria in adolescents,†Arch. Psychiatr. Nurs., vol. 35, no. 6, pp. 563–570, Dec. 2021. https://doi.org/10.1016/j.apnu.2021.08.009

M. Yang and G. Thung, “Classification of Trash for Recyclability Status,†pp. 1–6, 2016. https://doi.org/10.1145/2971648.2971731

S. López-Tapia, R. Molina, and A. K. Katsaggelos, “Deep learning approaches to inverse problems in imaging: Past, present and future,†Digit. Signal Process., vol. 119, p. 103285, Dec. 2021. https://doi.org/10.1016/j.dsp.2021.103285

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,†Communications of the ACM, vol. 60, no. 6, pp. 84–90, May 2017. https://doi.org/10.1145/3065386

C. Szegedy et al., “Going deeper with convolutions,†in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, vol. 07-12-June, pp. 1–9. https://doi.org/10.1109/CVPR.2015.7298594

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,†in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 2261–2269. https://doi.org/10.1109/CVPR.2017.243

W. Liu et al., “SSD: Single shot multibox detector,†Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9905 LNCS, pp. 21–37, 2016. https://doi.org/10.1007/978-3-319-46448-0_2

J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,†arXiv preprint arXiv:1804.02767 2018, [Online]. Available: http://arxiv.org/abs/1804.02767

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, 2017. https://doi.org/10.1109/TPAMI.2016.2577031

W. L. Mao, H. I. K. Fathurrahman, Y. Lee, and T. W. Chang, “EEG dataset classification using CNN method,†in Journal of physics: conference series, 2020, vol. 1456, no. 1, p. 12017. https://doi.org/10.1088/1742-6596/1456/1/012017

A. Lambora, K. Gupta, and K. Chopra, “Genetic Algorithm- A Literature Review,†Proc. Int. Conf. Mach. Learn. Big Data, Cloud Parallel Comput. Trends, Prespectives Prospect. Com. 2019, no. 1998, pp. 380–384, 2019. https://doi.org/10.1109/COMITCon.2019.8862255

S. Meng and W. -T. Chu, “A Study of Garbage Classification with Convolutional Neural Networks,†2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN), 2020, pp. 152-157. https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181311

C. Bircanoglu, M. Atay, F. Beser, O. Genc, and M. A. Kizrak, “RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks,†2018 IEEE Int. Conf. Innov. Intell. Syst. Appl. INISTA 2018, 2018. https://doi.org/10.1109/INISTA.2018.8466276

Kissablemt, “InceptionV3 Fine-tuning [97%] | Kaggle,†2020. https://www.kaggle.com/kissablemt/inceptionv3-fine-tuning-97 (accessed Apr. 16, 2020).

R. A. Aral, S. R. Keskin, M. Kaya, and M. Haciömeroǧlu, “Classification of TrashNet Dataset Based on Deep Learning Models,†Proc. - 2018 IEEE Int. Conf. Big Data, Big Data 2018, pp. 2058–2062, 2019. https://doi.org/10.1109/BigData.2018.8622212

Downloads

Published

2022-01-01

How to Cite

[1]
H. I. K. Fathurrahman, A. Ma’arif, and L.-Y. Chin, “The Development of Real-Time Mobile Garbage Detection Using Deep Learning”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 7, no. 3, pp. 472–478, Jan. 2022.

Issue

Vol. 7 No. 3 (2021): December

Section

Articles

License

Authors who publish with JITEKI agree to the following terms:

  1. Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

Lisensi Creative Commons

This work is licensed under a Creative Commons Attribution 4.0 International License

Similar Articles

1 2 3 > >> 

You may also start an advanced similarity search for this article.