Waste Classification Using Artificial Intelligence Techniques:Literature Review
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Abstract
waste is the total remnants of domestic, agricultural, industrial and productive human activities, all the trash left somewhere, the neglect of which threatens and harms public safety. Waste is divided into many types such as: Non-biodegradable waste, Hazardous waste, Industrial waste, Municipal solid waste, Agricultural waste.
Organic waste: It is fermentable waste such as food scraps and garden waste. Inorganic waste: It is waste that does not contain organic compounds, such as plastic, metals, and clothes. Also Solid waste: like mineral or glass materials, and it results from domestic, industrial and agricultural waste. It needs hundreds of years to decompose, and its presence poses an environmental threat. The efficiency and accuracy of conventional trash classification techniques are both low, Waste classification is the process of identifying and categorizing different types of waste based on their characteristics. Accurate waste classification is important for a number of reasons, including supporting recycling and other forms of resource recovery, protecting the environment and human health, and reducing the costs of waste management. Additionally, because of the vast amount of waste, unskilled people separate rubbish, which is less exact, takes more time, and isn't entirely practicable. Artificial intelligence and image processing, two powerful computing techniques, have advanced and now offer a variety of solutions. However, the current waste classification models still have several issues, such as poor classification accuracy or lengthy run times, because various wastes require various methods of disposal. The existing waste classification models driven by deep learning are not easy to achieve accurate results and still need to be improved due to the various architecture networks adopted. Aimed at solving these problems. Several methods and modules have been reviewed with the advantages of each are listed in Table 1.
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References
Amin, Z.M.A., Sami, K.N., and Hassan, R.: ‘An approach of classifying waste using transfer learning method’, International Journal on Perceptive and Cognitive Computing, 2021, 7, (1), pp. 41-52
Vo, A.H., Vo, M.T., and Le, T.: ‘A novel framework for trash classification using deep transfer learning’, IEEE Access, 2019, 7, pp. 178631-178639
Sami, K.N., Amin, Z.M.A., and Hassan, R.: ‘Waste Management Using Machine Learning and Deep Learning Algorithms’, International Journal on Perceptive and Cognitive Computing, 2020, 6, (2), pp. 97-106
Shi, C., Tan, C., Wang, T., and Wang, L.: ‘A waste classification method based on a multilayer hybrid convolution neural network’, Applied Sciences, 2021, 11, (18), pp. 8572
Huang, G.L., He, J., Xu, Z., and Huang, G.: ‘A combination model based on transfer learning for waste classification’, Concurrency and Computation: Practice and Experience, 2020, 32, (19), pp. e5751
Abou Baker, N., Zengeler, N., and Handmann, U.: ‘A Transfer Learning Evaluation of Deep Neural Networks for Image Classification’, Machine Learning and Knowledge Extraction, 2022, 4, (1), pp. 22-41
Zhang, Q., Yang, Q., Zhang, X., Bao, Q., Su, J., and Liu, X.: ‘Waste image classification based on transfer learning and convolutional neural network’, Waste Management, 2021, 135, pp. 150-157
Feng, J.-w., and Tang, X.-y.: ‘Office garbage intelligent classification based on inception-v3 transfer learning model’, in Editor (Ed.)^(Eds.): ‘Book Office garbage intelligent classification based on inception-v3 transfer learning model’ (IOP Publishing, 2020, edn.), pp. 012008
Alsabei, A., Alsayed, A., Alzahrani, M., and Al-Shareef, S.: ‘Waste Classification by Fine-Tuning Pre-trained CNN and GAN’, International Journal of Computer Science & Network Security, 2021, 21, (8), pp. 65-70
Carreira, J., Madeira, H., and Silva, J.G.: ‘Xception: A technique for the experimental evaluation of dependability in modern computers’, IEEE Transactions on Software Engineering, 1998, 24, (2), pp. 125-136
Zhao, J., Guo, W., Cui, S., Zhang, Z., and Yu, W.: ‘Convolutional neural network for SAR image classification at patch level’, in Editor (Ed.)^(Eds.): ‘Book Convolutional neural network for SAR image classification at patch level’ (IEEE, 2016, edn.), pp. 945-948
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C.: ‘Mobilenetv2: Inverted residuals and linear bottlenecks’, in Editor (Ed.)^(Eds.): ‘Book Mobilenetv2: Inverted residuals and linear bottlenecks’ (2018, edn.), pp. 4510-4520
Kalvankar, S., Pandit, H., and Parwate, P.: ‘Galaxy morphology classification using efficientnet architectures’, arXiv preprint arXiv:2008.13611, 2020
1Cp, P., Narayanan, S., Kommuri, V.S., Subramanian, S., Bijlani, K., Nagendra, S., and Podila, N.: ‘ICACCI-02 (A): Artificial Intelligence and Machine Learning/Data Engineering/Biocomputing (Regular Papers)’
Costa, B.S., Bernardes, A.C., Pereira, J.V., Zampa, V.H., Pereira, V.A., Matos, G.F., Soares, E.A., Soares, C.L., and Silva, A.F.: ‘Artificial intelligence in automated sorting in trash recycling’, in Editor (Ed.)^(Eds.): ‘Book Artificial intelligence in automated sorting in trash recycling’ (SBC, 2018, edn.), pp. 198-205
1Agarwal, C., and Sharma, A.: ‘Image understanding using decision tree based machine learning’, in Editor (Ed.)^(Eds.): ‘Book Image understanding using decision tree based machine learning’ (IEEE, 2011, edn.), pp. 1-8
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H.: ‘Mobilenets: Efficient convolutional neural networks for mobile vision applications’, arXiv preprint arXiv:1704.04861, 2017
Rishma, G., and Aarthi, R.: ‘Classification of Waste Objects Using Deep Convolutional Neural Networks’: ‘ICDSMLA 2020’ (Springer, 2022), pp. 533-542
1Yang, M., and Thung, G.: ‘Classification of trash for recyclability status’, CS229 project report, 2016, 2016, (1), pp. 3
Salmador, A., Pérez Cid, J., and Rodríguez Novelle, I.: ‘Intelligent garbage classifier’, 2008
Valente, M., Silva, H., Caldeira, J.M., Soares, V.N., and Gaspar, P.D.: ‘Detection of waste containers using computer vision’, Applied System Innovation, 2019, 2, (1), pp. 11
Bansal, S., Patel, S., Shah, I., Patel, P., Makwana, P., and Thakker, D.: ‘AGDC: Automatic garbage detection and collection’, arXiv preprint arXiv:1908.05849, 2019
2Bansal, M., Kumar, M., Sachdeva, M., and Mittal, A.: ‘Transfer learning for image classification using VGG19: Caltech-101 image data set’, Journal of Ambient Intelligence and Humanized Computing, 2021, pp. 1-12
Srivatsan, K., Dhiman, S., and Jain, A.: ‘Waste Classification using Transfer Learning with Convolutional Neural Networks’, in Editor (Ed.)^(Eds.): ‘Book Waste Classification using Transfer Learning with Convolutional Neural Networks’ (IOP Publishing, 2021, edn.), pp. 012010
Wu, F., and Lin, H.: ‘Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste’, Frontiers in Environmental Science, 2022, 10, pp. 2129
Xu, X., Qi, X., and Diao, X.: ‘Reach on waste classification and identification by transfer learning and lightweight neural network’, 2020
Liang, S., and Gu, Y.: ‘A deep convolutional neural network to simultaneously localize and recognize waste types in images’, Waste Management, 2021, 126, pp. 247-257
Gupta, T., Joshi, R., Mukhopadhyay, D., Sachdeva, K., Jain, N., Virmani, D., and Garcia-Hernandez, L.: ‘A deep learning approach based hardware solution to categorise garbage in environment’, Complex & Intelligent Systems, 2022, 8, (2), pp. 1129-1152
Yusiong, J.P.T.: 'An Ensemble of CNN-ELM Models for Trash Classification', Bulletin on Innovative Computing, Information and Control, 2022, 16, (09), pp. 943