Smart City Gnosys

Smart city article details

Title	Waste Classification With Small Datasets And Limited Resources
ID_Doc	61435
Authors	Ruiz V.; Sánchez Á.; Vélez J.F.; Raducanu B.
Year	2022
Published	Intelligent Systems Reference Library, 224
DOI	http://dx.doi.org/10.1007/978-3-031-06307-7_10
Abstract	Automatic waste recycling has become a very important societal challenge nowadays, raising people’s awareness for a cleaner environment and a more sustainable lifestyle. With the transition to Smart Cities, and thanks to advanced ICT solutions, this problem has received a new impulse. The waste recycling focus has shifted from general waste treating facilities to an individual responsibility, where each person should become aware of selective waste separation. The surge of the mobile devices, accompanied by a significant increase in computation power, has potentiated and facilitated this individual role. An automated image-based waste classification mechanism can help with a more efficient recycling and a reduction of contamination from residuals. Despite the good results achieved with the deep learning methodologies for this task, the Achille’s heel is that they require large neural networks which need significant computational resources for training and therefore are not suitable for mobile devices. To circumvent this apparently intractable problem, we will rely on knowledge distillation in order to transfer the network’s knowledge from a larger network (called ‘teacher’) to a smaller, more compact one, (referred as ‘student’) and thus making it possible the task of image classification on a device with limited resources. For evaluation, we considered as ‘teachers’ large architectures such as InceptionResNet or DenseNet and as ‘students’, several configurations of the MobileNets. We used the publicly available TrashNet dataset to demonstrate that the distillation process does not significantly affect system’s performance (e.g. classification accuracy) of the student network. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords

Similar Articles

Id	Similarity	Authors	Title	Published
23601	0.928	Kabilan B.; Sairam R.; Praveen M.	Enhanced Cnn Architecture For Accurate Waste Classification In Smart Cities	Proceedings of 5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024 (2024)
58394	0.927	Islam M.; Hasan S.M.M.; Rakib H.; Uddin P.; Mamun A.	Towards Sustainable Solutions: Effective Waste Classification Framework Via Enhanced Deep Convolutional Neural Networks	PLOS ONE, 20, 6 June (2025)
23663	0.924	Adhikari B.; Ranabhat R.K.; Rahman M.M.; Kashef R.	Enhanced Recyclenet For Efficient Waste Classification	2022 9th International Conference on Soft Computing and Machine Intelligence, ISCMI 2022 (2022)
4798	0.919	Lilhore U.K.; Simaiya S.; Dalal S.; Damaševičius R.	A Smart Waste Classification Model Using Hybrid Cnn-Lstm With Transfer Learning For Sustainable Environment	Multimedia Tools and Applications, 83, 10 (2024)
22313	0.916	Chauhan R.; Shighra S.; Madkhali H.; Nguyen L.; Prasad M.	Efficient Future Waste Management: A Learning-Based Approach With Deep Neural Networks For Smart System (Lads)	Applied Sciences (Switzerland), 13, 7 (2023)
24091	0.915	Srizon A.Y.; Sarker A.; Mamun M.A.; Faruk M.F.; Hasan S.M.M.	Enhancing Waste Categorization Using Ensemble Of Transfer Learning And Light-Weight Convolutional Neural Network	2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023 (2023)
7826	0.914	Saghana K.; Saranya P.; Mahesh Reddy A.; Keerthy Rai V.; Ramasubramanian B.; Sudhakaran P.	An Efficient Deep Learning Based Waste Management System For Sustainable Environment	3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025 (2025)
52263	0.914	Ramya R.; Vinitha Shree S.; Yogeshwari S.; Venkatesan S.	Solid Waste Identification And Classification Method Based On Feature Selection And Hybrid Resnet Cnn Models In Smart Environment	Lecture Notes in Networks and Systems, 730 LNNS (2023)
56836	0.914	Cai W.; Xie M.; Liu Y.; Yang X.	The Smart City Waste Classification Management System: Strategies And Applications Based On Computer Vision	Journal of Organizational and End User Computing, 36, 1 (2024)
1329	0.912	Gomathi K.; Narayanan L.K.	A Deep Learning Aided Smart Waste Classification System For Smart Cities	Proceedings - 2024 IEEE 16th International Conference on Communication Systems and Network Technologies, CICN 2024 (2024)