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Year 2023, Volume: 6 Issue: 2, 91 - 104, 31.08.2023

Mahir Kaya , Samet Ulutürk , Yasemin Çetin Kaya , Onur Altıntaş , Bülent Turan

https://doi.org/10.35377/saucis...1257100
Cited By: 3

Abstract

References

  • [1] S. Kaza, L. C. Yao, P. Bhada-Tata and F. Van Woerden, "A Global Snapshot of Solid Waste Management to 2050," 2018, [Online]. Available: https://elibrary.worldbank.org/doi/abs/10.1596/978-1-4648-1329-0. [Accessed: 15-Dec-2022].
  • [2] D. Hoornweg and P. Bhada-Tata, What a Waste: A Global Review of Solid Waste Management, World Bank, Washington DC USA, 2012.
  • [3] R. E. Sanderson, Environmental Protection Agency Office of Federal Activities’ Guidance on Incorporating EPA’s Pollution Prevention Strategy into the Environmental Review Process, EPA, Washington, DC, USA, 1993.
  • [4] O. Adedeji and Z. Wang, "Intelligent Waste Classification System Using Deep Learning Convolutional Neural Network," Procedia Manufacturing, vol. 35, pp. 607-612, 2019.
  • [5] A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Proc - Neural Information Processing System Conference, pp. 1-9, 2012.
  • [6] Y. LeCun, Y. Bengio, & G. Hinton. "Deep learning," Nature, vol. 521, pp. 436-444, 2015.
  • [7] F. S. Alrayes et al., "Waste classification using vision transformer based on multilayer hybrid convolution neural network," Urban Climate, vol. 49, pp. 1-14, 2023.
  • [8] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, & R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting.," Journal of Machine Learning Research, vol. 15, no.1, pp. 1929-1958, 2014.
  • [9] C. Tan, F. Sun, T. Kong, W. Zhang and C. Y. &. C. Liu, "A survey on deep transfer learning," Proc. - 27th International Conference on Artificial Neural Networks, pp. 270-279, 2018.
  • [10] J. Yosinski, C. Jeff , B. Yoshua ve L. Hod, "How transferable are features in deep neural networks?," Advances in neural information processing systems, 2014.
  • [11] K. He, X. Zhang, S. Ren and J. Sun, "Deep residual learning for image recognition" Proc – IEEE conference onComputer Vision and Pattern Recognition, pp. 770-778, 2016.
  • [12] G. Huang, Z. Liu, L. v. d. Maaten and K. Q. Weinberger, "Densely Connected Convolutional Networks," Proc - IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700-4708, 2017.
  • [13] C. Bircanoğlu, M. Atay, F. Beşer, Ö. Genç, & M. A. Kızrak, "RecycleNet: Intelligent waste sorting using deep neural networks," Proc - 2018 Innovations in intelligent systems and applications, pp. 1-7, 2018.
  • [14] C. Wang, J. Qin, C. Qu, X. Ran, C. L. b and B. Chen, "A Smart Municipal Waste Management System Based on Deep-Learning and Internet of Things," Waste Management, vol. 135, pp. 20-29, 2021.
  • [15] R. A. Aral, Ş. R. Keskin, M. Kaya and M. Hacıömeroğlu, "Classification of TrashNet Dataset Based on Deep Learning Models," Proc - International Conference on Big Data, pp. 2058-2062, 2018.
  • [16] Q. Zhang, Q. Yang, X. Zhang, Q. Bao, J. Su and X. Liu, "Waste image classification based on transfer learning and convolutional neural network," Waste Management, vol. 135, pp. 150-157, 2021.
  • [17] D. Gyawali, A. Regmi, A. Shakya, A. Gautam and S. Shrestha, "Comparative Analysis of Multiple Deep CNN Models for Waste Classification," 2020, [Online]. Available: https://arxiv.org/abs/2004.02168. [Accessed: 10-Dec-2022].
  • [18] S. L. Rabano, M. K. Cabatuan, E. Sybingco, E. P. Dadios and E. J. Calilung, "Common Garbage Classification Using MobileNet," Proc - IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, pp. 1-4, 2018.
  • [19] Z. Feng, Yang, J., Chen, L., Chen, Z., & Li, L., "An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet," International Journal of Environmental Research and Public Health, vol. 19, no. 23, pp. 1-18, 2022.
  • [20] K. Lin, Zhao et al, "Applying a deep residual network coupling with transfer learning for recyclable waste sorting," Environmental Science and Pollution Research, vol. 29, no. 60, pp. 91081-91095, 2022.
  • [21] C. Shi, C. Tan, T. Wang and L. Wang, "A Waste Classification Method Based on a Multilayer Hybrid Convolution Neural Network," Applied Science, vol. 11, no 18, pp. 1-19, 2021.
  • [22] Z. Yang, Xia, Z., Yang, G., & Lv, Y. "A Garbage Classification Method Based on a Small Convolution Neural Network,” Sustainability, vol. 14, no. 22, pp. 1-16, 2022.
  • [23] J. Bobulski and M. Kubanek, "Deep Learning for Plastic Waste Classification System," Applied Computational Intelligence and Soft Computing, vol. 2021, pp. 1-7, 2021.
  • [24] J.-R. Riba, R. Cantero, P. Riba-Mosoll and R. Puig, "Post-Consumer Textile Waste Classification through Near-Infrared Spectroscopy, using an Advanced Deep Learning Approach," Polymers, vol. 14, no. 12, pp. 1-14, 2022.
  • [25] B. G. Tran, & D. L. Nguyen. “Simple and Efficient Convolutional Neural Network for Trash Classification,” Proc - Annals of Computer Science and Information Systems, pp. 255-260, 2022.
  • [26] N. C. A. Sallang, M. T. Islam, M. S. Islam and H. Arshad, " A CNN-Based Smart Waste Management System Using TensorFlow Lite and LoRa-GPS Shield in Internet of Things Environment," IEEE Access, vol. 9, pp. 153560-153574, 2021.
  • [27] D. O. Melinte, A.-M. Travediu and D. N. Dumitriu, "Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification," Applied Sciences, vol. 10, no. 20, pp. 1-18, 2020.
  • [28] P. Nowakowski and T. Pamula, "Application of Deep Learning Object Classifier to Improve E-waste Collection Planning" Waste Management, vol. 109, pp. 1-9, 2020.
  • [29] M. Yang, and G. Thung, "Classification of trash for recyclability status." CS229 project report 2016.1 (2016): 3.
  • [30] F. Hu, G.-S. Xia, J. Hu and L. Zhang, "Transfering Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery," Remote Sensing, vol. 7, no. 11, pp. 14680-14707, 2011.
  • [31] R. Jain, P. Nagrath, G. Kataria, V. S. Kaushik, & D. J. Hemanth, "Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning," Measurement, vol. 165, pp. 1-10, 2020.
  • [32] L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel and M. A.-A. &. L. Farhan, "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions," Journal of Big Data, vol. 8, pp. 1-74, 2021.
  • [33] S. K. Sundararajan, B. Sankaragomathi ve D. S. Priya, "Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images," Journal of Medical Systems, vol. 43, pp. 1-9, 2019.
  • [34] G. Li and N. Li, "Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network," Electronic Commerce Research, vol. 19, pp. 779-800, 2019.
  • [35] X. Y. Wu, "A hand gesture recognition algorithm based on DC-CNN," Multimedia Tools and Applications, vol. 79, pp. 9193-9205, 2020.
  • [36] S. V. Stehman, "Selecting and interpreting measures of thematic classification accuracy," Remote Sensing of Environment, vol. 62, no .1, pp. 77-89, 1997.
  • [37] S. M. Piryonesi and T. E. El-Diraby, "Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index," Journal of Infrastructure Systems, vol. 26, no. 1, pp. 1-25, 2020.
  • [38] D. M. W. Powers, "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation," Journal of Machine Learning Technologies, vol. 2, pp. 37-63, 2011.
  • [39] K. M. Ting, C. Sammut and G. I. Webb, Encyclopedia of machine learning, New York: Springer Science & Business Media, 2011.
  • [40] M. Talo, U. B. Baloglu, Ö. Yıldırım and U. R. Acharya, "Application of deep transfer learning for automated brain abnormality classification using MR images," Cognitive Systems Research, vol. 54, pp. 176-188, 2019.
  • [41] Y. Çetin-Kaya, M. Kaya & A. Akdağ, "Route Optimization for Medication Delivery of Covid-19 Patients with Drones," Gazi University Journal of Science Part C: Design and Technology, vol. 9, no. 3, pp. 478-491, 2021.
  • [42] M. Kaya, and Y. Çetin-Kaya, "Seamless computation offloading for mobile applications using an online learning algorithm,” Computing, vol. 103, no.5, pp. 771-799, 2021.

Optimization of Several Deep CNN Models for Waste Classification

Year 2023, Volume: 6 Issue: 2, 91 - 104, 31.08.2023

Mahir Kaya , Samet Ulutürk , Yasemin Çetin Kaya , Onur Altıntaş , Bülent Turan

https://doi.org/10.35377/saucis...1257100
Cited By: 3

Abstract

With urbanization, population, and consumption on the rise, urban waste generation is steadily increasing. Consequently, waste management systems have become integral to city life, playing a critical role in resource efficiency and environmental protection. Inadequate waste management systems can adversely affect the environment, human health, and the economy. Accurate and rapid automatic waste classification poses a significant challenge in recycling. Deep learning models have achieved successful image classification in various fields recently. However, the optimal determination of many hyperparameters is crucial in these models. In this study, we developed a deep learning model that achieves the best classification performance by optimizing the depth, width, and other hyperparameters. Our six-layer Convolutional Neural Network (CNN) model with the lowest depth and width produced a successful result with an accuracy value of 89% and an F1 score of 88%. Moreover, several state-of-the-art CNN models such as VGG19, DenseNet169, ResNet101, Xception, InceptionV3, RegnetX008, RegnetY008, EfficientNetV2S trained with transfer learning and fine-tuning. Extensive experimental work has been done to find the optimal hyperparameters with GridSearch. Our most comprehensive DenseNet169 model, which we trained with fine-tuning, provided an accuracy value of 96.42% and an F1 score of 96%. These models can be successfully used in a variety of waste classification automation.

Keywords

CNN deep learning trash optimization

References

  • [1] S. Kaza, L. C. Yao, P. Bhada-Tata and F. Van Woerden, "A Global Snapshot of Solid Waste Management to 2050," 2018, [Online]. Available: https://elibrary.worldbank.org/doi/abs/10.1596/978-1-4648-1329-0. [Accessed: 15-Dec-2022].
  • [2] D. Hoornweg and P. Bhada-Tata, What a Waste: A Global Review of Solid Waste Management, World Bank, Washington DC USA, 2012.
  • [3] R. E. Sanderson, Environmental Protection Agency Office of Federal Activities’ Guidance on Incorporating EPA’s Pollution Prevention Strategy into the Environmental Review Process, EPA, Washington, DC, USA, 1993.
  • [4] O. Adedeji and Z. Wang, "Intelligent Waste Classification System Using Deep Learning Convolutional Neural Network," Procedia Manufacturing, vol. 35, pp. 607-612, 2019.
  • [5] A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Proc - Neural Information Processing System Conference, pp. 1-9, 2012.
  • [6] Y. LeCun, Y. Bengio, & G. Hinton. "Deep learning," Nature, vol. 521, pp. 436-444, 2015.
  • [7] F. S. Alrayes et al., "Waste classification using vision transformer based on multilayer hybrid convolution neural network," Urban Climate, vol. 49, pp. 1-14, 2023.
  • [8] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, & R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting.," Journal of Machine Learning Research, vol. 15, no.1, pp. 1929-1958, 2014.
  • [9] C. Tan, F. Sun, T. Kong, W. Zhang and C. Y. &. C. Liu, "A survey on deep transfer learning," Proc. - 27th International Conference on Artificial Neural Networks, pp. 270-279, 2018.
  • [10] J. Yosinski, C. Jeff , B. Yoshua ve L. Hod, "How transferable are features in deep neural networks?," Advances in neural information processing systems, 2014.
  • [11] K. He, X. Zhang, S. Ren and J. Sun, "Deep residual learning for image recognition" Proc – IEEE conference onComputer Vision and Pattern Recognition, pp. 770-778, 2016.
  • [12] G. Huang, Z. Liu, L. v. d. Maaten and K. Q. Weinberger, "Densely Connected Convolutional Networks," Proc - IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700-4708, 2017.
  • [13] C. Bircanoğlu, M. Atay, F. Beşer, Ö. Genç, & M. A. Kızrak, "RecycleNet: Intelligent waste sorting using deep neural networks," Proc - 2018 Innovations in intelligent systems and applications, pp. 1-7, 2018.
  • [14] C. Wang, J. Qin, C. Qu, X. Ran, C. L. b and B. Chen, "A Smart Municipal Waste Management System Based on Deep-Learning and Internet of Things," Waste Management, vol. 135, pp. 20-29, 2021.
  • [15] R. A. Aral, Ş. R. Keskin, M. Kaya and M. Hacıömeroğlu, "Classification of TrashNet Dataset Based on Deep Learning Models," Proc - International Conference on Big Data, pp. 2058-2062, 2018.
  • [16] Q. Zhang, Q. Yang, X. Zhang, Q. Bao, J. Su and X. Liu, "Waste image classification based on transfer learning and convolutional neural network," Waste Management, vol. 135, pp. 150-157, 2021.
  • [17] D. Gyawali, A. Regmi, A. Shakya, A. Gautam and S. Shrestha, "Comparative Analysis of Multiple Deep CNN Models for Waste Classification," 2020, [Online]. Available: https://arxiv.org/abs/2004.02168. [Accessed: 10-Dec-2022].
  • [18] S. L. Rabano, M. K. Cabatuan, E. Sybingco, E. P. Dadios and E. J. Calilung, "Common Garbage Classification Using MobileNet," Proc - IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, pp. 1-4, 2018.
  • [19] Z. Feng, Yang, J., Chen, L., Chen, Z., & Li, L., "An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet," International Journal of Environmental Research and Public Health, vol. 19, no. 23, pp. 1-18, 2022.
  • [20] K. Lin, Zhao et al, "Applying a deep residual network coupling with transfer learning for recyclable waste sorting," Environmental Science and Pollution Research, vol. 29, no. 60, pp. 91081-91095, 2022.
  • [21] C. Shi, C. Tan, T. Wang and L. Wang, "A Waste Classification Method Based on a Multilayer Hybrid Convolution Neural Network," Applied Science, vol. 11, no 18, pp. 1-19, 2021.
  • [22] Z. Yang, Xia, Z., Yang, G., & Lv, Y. "A Garbage Classification Method Based on a Small Convolution Neural Network,” Sustainability, vol. 14, no. 22, pp. 1-16, 2022.
  • [23] J. Bobulski and M. Kubanek, "Deep Learning for Plastic Waste Classification System," Applied Computational Intelligence and Soft Computing, vol. 2021, pp. 1-7, 2021.
  • [24] J.-R. Riba, R. Cantero, P. Riba-Mosoll and R. Puig, "Post-Consumer Textile Waste Classification through Near-Infrared Spectroscopy, using an Advanced Deep Learning Approach," Polymers, vol. 14, no. 12, pp. 1-14, 2022.
  • [25] B. G. Tran, & D. L. Nguyen. “Simple and Efficient Convolutional Neural Network for Trash Classification,” Proc - Annals of Computer Science and Information Systems, pp. 255-260, 2022.
  • [26] N. C. A. Sallang, M. T. Islam, M. S. Islam and H. Arshad, " A CNN-Based Smart Waste Management System Using TensorFlow Lite and LoRa-GPS Shield in Internet of Things Environment," IEEE Access, vol. 9, pp. 153560-153574, 2021.
  • [27] D. O. Melinte, A.-M. Travediu and D. N. Dumitriu, "Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification," Applied Sciences, vol. 10, no. 20, pp. 1-18, 2020.
  • [28] P. Nowakowski and T. Pamula, "Application of Deep Learning Object Classifier to Improve E-waste Collection Planning" Waste Management, vol. 109, pp. 1-9, 2020.
  • [29] M. Yang, and G. Thung, "Classification of trash for recyclability status." CS229 project report 2016.1 (2016): 3.
  • [30] F. Hu, G.-S. Xia, J. Hu and L. Zhang, "Transfering Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery," Remote Sensing, vol. 7, no. 11, pp. 14680-14707, 2011.
  • [31] R. Jain, P. Nagrath, G. Kataria, V. S. Kaushik, & D. J. Hemanth, "Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning," Measurement, vol. 165, pp. 1-10, 2020.
  • [32] L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel and M. A.-A. &. L. Farhan, "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions," Journal of Big Data, vol. 8, pp. 1-74, 2021.
  • [33] S. K. Sundararajan, B. Sankaragomathi ve D. S. Priya, "Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images," Journal of Medical Systems, vol. 43, pp. 1-9, 2019.
  • [34] G. Li and N. Li, "Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network," Electronic Commerce Research, vol. 19, pp. 779-800, 2019.
  • [35] X. Y. Wu, "A hand gesture recognition algorithm based on DC-CNN," Multimedia Tools and Applications, vol. 79, pp. 9193-9205, 2020.
  • [36] S. V. Stehman, "Selecting and interpreting measures of thematic classification accuracy," Remote Sensing of Environment, vol. 62, no .1, pp. 77-89, 1997.
  • [37] S. M. Piryonesi and T. E. El-Diraby, "Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index," Journal of Infrastructure Systems, vol. 26, no. 1, pp. 1-25, 2020.
  • [38] D. M. W. Powers, "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation," Journal of Machine Learning Technologies, vol. 2, pp. 37-63, 2011.
  • [39] K. M. Ting, C. Sammut and G. I. Webb, Encyclopedia of machine learning, New York: Springer Science & Business Media, 2011.
  • [40] M. Talo, U. B. Baloglu, Ö. Yıldırım and U. R. Acharya, "Application of deep transfer learning for automated brain abnormality classification using MR images," Cognitive Systems Research, vol. 54, pp. 176-188, 2019.
  • [41] Y. Çetin-Kaya, M. Kaya & A. Akdağ, "Route Optimization for Medication Delivery of Covid-19 Patients with Drones," Gazi University Journal of Science Part C: Design and Technology, vol. 9, no. 3, pp. 478-491, 2021.
  • [42] M. Kaya, and Y. Çetin-Kaya, "Seamless computation offloading for mobile applications using an online learning algorithm,” Computing, vol. 103, no.5, pp. 771-799, 2021.
There are 42 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Articles
Authors

Mahir Kaya 0000-0001-9182-271X

Samet Ulutürk 0000-0002-7765-6281

Yasemin Çetin Kaya 0000-0002-6745-7705

Onur Altıntaş 0009-0009-2711-5119

Bülent Turan 0000-0003-0673-469X

Early Pub Date August 27, 2023
Publication Date August 31, 2023
Submission Date February 28, 2023
Acceptance Date July 17, 2023
Published in Issue Year 2023Volume: 6 Issue: 2

Cite

IEEE M. Kaya, S. Ulutürk, Y. Çetin Kaya, O. Altıntaş, and B. Turan, “Optimization of Several Deep CNN Models for Waste Classification”, SAUCIS, vol. 6, no. 2, pp. 91–104, 2023, doi: 10.35377/saucis...1257100.

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