Abstract
Waste segregation has become a daunting problem in the twenty-first century, as careless waste disposal manifests significant ecological and health concerns. Existing approaches to waste disposal primarily rely on incineration or land filling, neither of which are sustainable. Hence, responsible recycling and then adequate disposal is the optimal solution promoting both environment-friendly practices and reuse. In this paper, a computer vision-based approach for automated waste classification across multiple classes of waste products is proposed. We focus on improving the quality of existing datasets using data augmentation and image processing techniques. We also experiment with transfer learning based models such as ResNet and VGG for fast and accurate classification. The models were trained, validated, and tested on the benchmark TrashNet and TACO datasets. During experimental evaluation, the proposed model achieved 93.13% accuracy on TrashNet and outperformed state-of-the-art models by a margin of 16% on TACO.
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References
Thung G, Yang M (2016) Classification of trash for recyclability status [Online]. Available: https://github.com/garythung/trashnet
Proença PF, Simões P (2020) Taco: trash annotations in context for litter detection. arXiv preprint arXiv:2003.06975
Majchrowska S, Mikolajczyk A, Ferlin M, Klawikowska Z, Plantykow MA, Kwasigroch A, Majek K (2021) Waste detection in Pomerania: non-profit project for detecting waste in environment. arXiv:2105.06808
Azis FA, Suhaimi H, Abas E (2020) Waste classification using convolutional neural network. In: Proceedings of the 2020 2nd international conference on information technology and computer communications, ITCC 2020. Association for Computing Machinery, New York, NY, USA, pp 9–13
Adedeji O, Wang Z (2019) Intelligent waste classification system using deep learning convolutional neural network. Procedia Manuf 35:607–612. In: The 2nd international conference on sustainable materials processing and manufacturing, SMPM 2019, 8–10 Mar 2019, Sun City, South Africa
Mao W-L, Chen W-C, Wang C-T, Lin Y-H (2021) Recycling waste classification using optimized convolutional neural network. Resour Conserv Recycl 164:105132
Meng S, Chu W-T (2020) A study of garbage classification with convolutional neural networks. In: 2020 Indo–Taiwan 2nd international conference on computing, analytics and networks (Indo-Taiwan ICAN), pp 152–157
Ozkaya U, Seyfi L (2019) Fine-tuning models comparisons on garbage classification for recyclability. arXiv preprint arXiv:1908.04393
Masand A, Chauhan S, Jangid M, Kumar R, Roy S (2021) Scrapnet: an efficient approach to trash classification. IEEE Access 9:130947–130958
Rishma G, Aarthi R (2022) Classification of waste objects using deep convolutional neural networks. In: Kumar A, Senatore S, Gunjan VK (eds) ICDSMLA 2020. Springer, Singapore, pp 533–542
Yadav S, Shanmugam A, Hima V, Suresh N (2021) Wasteclassification and segregation: machine learning and IoT approach. In: 2021 2nd international conference on intelligent engineering and management (ICIEM), pp 233–238
Wu H, Zheng S, Zhang J, Huang K (2017) GP-GAN: towards realistic high-resolution image blending. arXiv:1703.07195
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville, Bengio Y (2014) Generative adversarial networks
Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248–255
He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv:1512.03385
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
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Kumsetty, N.V., Nekkare, A.B., Sowmya Kamath, S., Anand Kumar, M. (2023). An Approach for Waste Classification Using Data Augmentation and Transfer Learning Models. In: Kumar Singh, K., Bajpai, M.K., Sheikh Akbari, A. (eds) Machine Vision and Augmented Intelligence. Lecture Notes in Electrical Engineering, vol 1007. Springer, Singapore. https://doi.org/10.1007/978-981-99-0189-0_27
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DOI: https://doi.org/10.1007/978-981-99-0189-0_27
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