A Framework for Dry Waste Detection Based on a Deep Convolutional Neural Network

Ataee, A.; Kazemitabar, J.; Najafi, M.

doi:10.5829/ijee.2020.11.04.01

Document Type : Original Article

Authors

¹ Department of Electrical Engineering, Babol Noshirvani University of Technology, Babol, Iran

² Department of Electrical and Computer Engineering. Arak University of Technology, Arak, Iran

https://doi.org/10.5829/ijee.2020.11.04.01

Abstract

Due to lack of proper regulations in many areas of the world, consumers are not mandated to waste sorting at the origin of the source. Moreover, human sorting often suffers from human errors and low accuracy. In the intelligent detection system, it is attempted to break down a variety of household wastes including plastic bottles, glass, metals, paper bags, compact plastics, paper and disposable containers. In this paper, a real waste image system is investigated using the deep convolutional neural network and a remarkable accuracy of 92.76% was achieved.

Keywords

References

1. Adedeji, O. & Wang, Z., 2019. Intelligent Waste Classification System Using Deep Learning Convolutional Neural Network. Procedia Manufacturing, 35: 607-612. https://doi.ir/10.1016/j.promfg.2019.05.086

2. Gupta, P. K., Shree, V., Hiremath, L. & Rajendran, S., 2019. The Use of Modern Technology in Smart Waste Management and Recycling: Artificial Intelligence and Machine Learning, in Recent Advances in Computational Intelligence. Springer. p. 173-188.

3. Tehrani, A. & Karbasi, H., 2017. A Novel Integration of Hyper-Spectral Imaging and Neural Networks to Process Waste Electrical and Electronic Plastics. IEEE Conference on Technologies for Sustainability (SusTech), pp: 1-5. https://doi.org/10.1109/SusTech.2017.8333533

4. Zhihong, C., Hebin, Z., Yanbo, W., Binyan, L. & Yu, L., 2017. A Vision-Based Robotic Grasping System Using Deep Learning for Garbage Sorting. 36th Chinese Control Conference (CCC), pp: 11223-11226. https://doi.org/10.23919/ChiCC.2017.8029147

5. Sakr, G. E., Mokbel, M., Darwich, A., Khneisser, M. N. & Hadi, A., 2016. Comparing Deep Learning and Support Vector Machines for Autonomous Waste Sorting. IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), pp: 207-212. https://doi.org/10.1109/IMCET.2016.7777453

6. Dhulekar, P., Gandhe, S. & Mahajan, U. P., 2018. Development of Bottle Recycling Machine Using Machine Learning Algorithm. International Conference On Advances in Communication and Computing Technology (ICACCT), pp: 515-519. https://doi.org/10.1109/ICACCT.2018.8529483

7. Dharmana, M. M., Mahathi, L. U., Indarapu, A. & Reddy, B. H. M., 2019. Plastic Segregation Machine Using Audio Signal Analysis. 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), pp: 982-985. https://doi.org/10.1109/ICECA.2019.8821842

8. Jähne, B., 2005. Digital Image Processing Springer.

9. Karadimas, N. V., Papatzelou, K. & Loumos, V. G., 2007. Optimal Solid Waste Collection Routes Identified by the Ant Colony System Algorithm. Waste management research, 25(2): 139-147. https://doi.org/10.1177/0734242X07071312

10. McHenry, K. & Ponce, J., 2006. A Geodesic Active Contour Framework for Finding Glass. in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), pp: 1038-1044. https://doi.org/10.1109/CVPR.2006.28

11. Simonyan, K. & Zisserman, A., 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:.

12. Oka, T., Takeda, A., Sasaki, S., Ogawa, J., Fukui, S., Sato, T., Scheiter, J., Häßler, W., Katsuki, J. & Miura, A., 2018. Magnetic Flux Invasion and Field-Capturing in Pulsed-Field Magnetization for Layered Mgb2 Bulk Magnets. IEEE Transactions on Applied Superconductivity, 28(4): 1-4. https://doi.org/10.1109/TASC.2018.2792530

13. Siddappaji, Sujatha, K. & Radha, R. C., Technologies for Segregation and Management of Solid Waste: A Review, in International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS). 2016: Pudukkottai. p. 1-4.

14. Volland, A., Multiplexed Nir Spectroscopic Sensors and Nir Spetroscopic Imagimg: Two Solutions for Sensor Based Waste Sorting in Comparison, in Waste-to-Resources III International Symposium MBT & MRF. 2009. p. 454-462.

15. Prasanna, M. A., Kaushal, S. V. & Mahalakshmi, P., 2018. Survey on Identification and Classification of Waste for Efficient Disposal and Recycling International Journal of Engineering and Technology, 7520-523. https://doi.org/10.14419/ijet.v7i2.8.10513

16. Tarmudi, Z., Abdullah, M. L. & Tap, A. O. M., 2009. An Overview of Municipal Solid Wastes Generation in Malaysia. Jurnal Teknologi, 51(1): 1-15. https://doi.org/10.11113/jt.v51.142

17. Rosebrock, A., 2017. Deep Learning for Computer Vision with Python: Starter Bundle. PyImageSearch.

18. Ciregan, D., Meier, U. & Schmidhuber, J., 2012. Multi-Column Deep Neural Networks for Image Classification. IEEE conference on computer vision and pattern recognition, pp: 3642-3649. https://doi.org/10.1109/CVPR.2012.6248110

19. Ren, J. S. & Xu, L., 2015. On Vectorization of Deep Convolutional Neural Networks for Vision Tasks. arXiv preprint arXiv:.07338,

20. Abrahams, S., Erwitt, E., Scarpinelli, A. & Hafner, D., 2016. Tensorflow for Machine Intelligence: A Hands-on Introduction to Learning Algorithms. Bleeding Edge Press.

21. Belongie, S., Malik, J. & Puzicha, J., 2002. Shape Matching and Object Recognition Using Shape Contexts. IEEE transactions on pattern analysis machine intelligence, 24(4): 509-522. https://doi.org/10.1109/34.993558

Iranica Journal of Energy & Environment

A Framework for Dry Waste Detection Based on a Deep Convolutional Neural Network

References

References

Volume 11, Issue 4
December 2020
Pages 248-252

A Framework for Dry Waste Detection Based on a Deep Convolutional Neural Network

References

References

Volume 11, Issue 4December 2020Pages 248-252

Volume 11, Issue 4
December 2020
Pages 248-252