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A Machine Vision Model for Solid Waste Classification using Smart Intelligence

Xiaolan Zeng, Sulala M. Z. F. Al-Hamadani

Abstract


The waste sorting is carried out using machines still require high precision is discriminating objects into appropriate categories. The waste objects are fed in a conveyor where the objects are separated manually and by tools. The cost of separating manually becomes high and time consuming with humans and the performance of separating tools depends on the precision of classifying the objects automatically. The classification of each object is presently aided through computer vision and machine learning technology. Data mining is a process of extracting useful information and patterns hidden inside large volume of data. Machine learning is a division of data mining through which algorithms gain the ability to improve their performance through self-learning. Data mining and machine learning algorithms are used in variety of data formats which includes numerical data, signals, multimedia formats, spatial data, sensor data. In this paper a novel idea for the solid waste classifications are given along with the future enhancements.


Keywords


Digital Image Processing, Solid Waste Classification, Machine Learning.

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References


Adedeji, O., & Wang, Z. (2019). Intelligent Waste Classification System Using Deep Learning Convolutional Neural Network. Procedia Manufacturing, 35, 607-612.

Aral, R. A., Keskin, S. R., Kaya, M., & Haciömeroglu, M. (2018). Classification of TrashNet Dataset Based on Deep Learning Models. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 2058-2062)

Arebey, M., Hannan, M. A., Begum, R. A., & Basri, H. (2012). Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach. Journal of environmental management, 104, 9-18.

Bandal, A., & Thirugnanam, M. (2019). Hybrid approach for fruits quality prediction using image processing and sensors technique. International Journal of Sustainable Agricultural Management and Informatics, 5(2-3), 201-214

Tchernykh, A., Schwiegelsohn, U., Alexandrov, V. and Talbi, E.G., 2015, “Towards understanding uncertainty in cloud computing resource provisioning”, Procedia Computer Science, vol.51, pp.1772-1781.

Budzianowski, W.M., 2012. Sustainable biogas energy in Poland: prospects and challenges. Renewable and Sustainable Energy Review 16 (1), 342-3

Gottschling, A., & Schabel, S. (2016). Pattern classification system for the automatic analysis of paper for recycling. International Journal of Applied Pattern Recognition, 3(1), 38-58.

Gundupalli, S. P., Hait, S., & Thakur, A. (2017). Multi-material classification of dry recyclables from municipal solid waste based on thermal imaging. Waste management, 70, 13-21.

Hannan, M. A., Arebey, M., Begum, R. A., Basri, H., & Al Mamun, M. A. (2016). Content-based image retrieval system for solid waste bin level detection and performance evaluation. Waste management, 50, 10-19.

Islam, M. S., Hannan, M. A., Basri, H., Hussain, A., & Arebey, M. (2014). Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier. Waste management, 34(2), 281-290.

Liu, Y., Fung, K. C., Ding, W., Guo, H., Qu, T., & Xiao, C. (2018). Novel Smart Waste Sorting System based on Image Processing Algorithms: SURF-BoW and Multi-class SVM. Computer and Information Science, 11(3), 35-49.

Milinda, H. G. T., & Madhusanka, B. G. D. A. (2017). Mud and dirt separation method for floor cleaning robot. In 2017 Moratuwa Engineering Research Conference (MERCon) (pp. 316-320).

Mittal, G., Yagnik, K. B., Garg, M., & Krishnan, N. C. (2016). Spotgarbage: smartphone app to detect garbage using deep learning. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 940-945).

Rabano, S. L., Cabatuan, M. K., Sybingco, E., Dadios, E. P., & Calilung, E. J. Common Garbage Classification Using MobileNet. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-4).

Ramalingam, B., Lakshmanan, A., Ilyas, M., Le, A., & Elara, M. (2018). Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application. Applied Sciences, 8(12), 2649.

Sakr, G. E., Mokbel, M., Darwich, A., Khneisser, M. N., & Hadi, A. (2016). Comparing deep learning and support vector machines for autonomous waste sorting. In 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET) (pp. 207-212).

Sreelakshmi, K., Akarsh, S., Vinayakumar, R., & Soman, K. P. (2019). Capsule Neural Networks and Visualization for Segregation of Plastic and Non-Plastic Wastes. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS) (pp. 631-636).

Srigul, W., Inrawong, P., & Kupimai, M. (2016). Plastic classification base on correlation of RGB color. In 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 1-5).

Srinilta, C., & Kanharattanachai, S. (2019). Municipal Solid Waste Segregation with CNN. In 2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST) (pp. 1-4)

Tehrani, A., & Karbasi, H. (2017). A novel integration of hyper-spectral imaging and neural networks to process waste electrical and electronic plastics. In 2017 IEEE Conference on Technologies for Sustainability (SusTech) (pp. 1-5).

Valente, M., Silva, H., Caldeira, J. M., Soares, V. N., & Gaspar, P. D. (2019). Detection of Waste Containers Using Computer Vision. Applied System Innovation, 2(1), 11

Wang, Z., Peng, B., Huang, Y., & Sun, G. (2019). Classification for plastic bottles recycling based on image recognition. Waste management, 88, 170-181.

Xu, P., Li, Z., Hong, T., & Ni, H. (2018). Fruit fly image segmentation and species determination algorithm. International Journal of Information and Communication Technology, 13(2), 176-185.

Zeng, Z., Guan, L., Yi, S., Zhu, Y., Liu, Q., Tong, Q., & Zeng, S. (2017). An object classification method based on the improved bacterial foraging optimisation algorithm. International Journal of Wireless and Mobile Computing, 12(2), 166-173.

Zhang, J., Qiu, Y., Chen, J., Guo, J., Chen, J., & Chen, S. (2019). Three dimensional object segmentation based on spatial adaptive projection for solid waste. Neurocomputing, 328, 122-134.


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