Real Life Smart Waste Management System [DRY, WET, RECYCLE, ELECTRONIC & MEDICAL]
DOI:
https://doi.org/10.32628/CSEIT2174135Keywords:
Automatic Waste Segregation, Convolutional Neural Network, MobileNetV2, Computer Vision Library, Object Detection, Object Recognition, Object Classification.Abstract
The problem of real-life smart waste management system can be solved using automatic waste segregation. In particular, the focus of the article is on the problem of detection (i.e., waste classification). In these 5 classes of waste are taken and segregated them into 5 categories namely dry, wet, recycle, electronic and medical. This system will automatically detect the waste object and segregate it into the respective category. The use of machine learning allowed improving the model with more accuracy. Convolutional Neural Networks (CNN) algorithm which is best used for image classification is used for object detection. The models that was trained are ResNet50, VGG16, InceptionV3 and MobileNetV2. Finally, when compared to the results of all these models, MobileNetV2 has given us the best and highest accuracy of about 98% and 99% respectively.
References
- Flores, Myra and Jose Tan. “Literature Review of Automated Waste Segregation System Using Machine Learning: A Comprehensive Analysis.” International journal of simulation: systems, science and technology (2019).
- Funch, Oliver Istad et al. “Detecting glass and metal in consumer trash bags during waste collection using convolutional neural networks.” Waste management 119 (2020).
- Khoury, Ibrahim et al. “ContamiNet: Detecting Contamination in Municipal Solid Waste.” ArXiv abs/1911.04583 (2019).
- Dev, B. “Automatic Waste Segregation using Image Processing and Machine Learning.” International Journal for Research in Applied Science and Engineering Technology 6 (2018).
- AdhithyaPrasanna, M. et al. “Survey on identification and classification of waste for efficient disposal and recycling.” International journal of engineering and technology 7 (2018).
- Kim, Jeong-guk et al. “A study on object recognition using deep learning for optimizing categorization of radioactive waste.” Progress in Nuclear Energy 130 (2020).
- Patel, Deep et al. “Garbage Detection using Advanced Object Detection Techniques.” 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) (2021).
- Sakr, G. et al. “Comparing deep learning and support vector machines for autonomous waste sorting.” 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET) (2016).
- Mittal, Gaurav et al. “Spot Garbage: smartphone app to detect garbage using deep learning.” Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (2016).
- Nieto, P. et al. “A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance.” J. Computer. Appl. Math. 330 (2018).
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.