Abstract
Garbage management is one of the most obvious challenges that humanity will face in the near future. The primary need is to produce a dust-free atmosphere. It is difficult to have a clean waste system in large cities. An automated system is put up beside the trashcan to keep track of the individual’s rubbish disposal habits. The majority of people, out of laziness, throw the dust out of the bin. The camera positioned over the trashcan records whether or not dust is deposited in the bin. When the dust is removed from the bin, the camera catches the defaulter’s face. The defaulter’s facial data is compared to an existing data collection and identified. The kit’s warning system will issue a warning signal to the appropriate user. We describe a smart waste management system that requires the user to handle dust with caution. The user database is hosted in the cloud and may be viewed from anywhere in the city.
Keywords
- Garbage management
- Face recognition
- Deep stack model
- Automated warning system
- Raspberry Pi camera
This is a preview of subscription content, access via your institution.
Buying options
References
Singh, G., Goel, A.K.: Face detection and recognition system using digital image processing. In: 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 348–352 (2020). https://doi.org/10.1109/ICIMIA48430.2020.9074838
Bah, S.M., Ming, F.: An improved face recognition algorithm and its application in attendance management system. Array 5, 100014 (2020). https://doi.org/10.1016/j.array.2019.100014. (ISSN: 2590-0056)
Vinod, V.M., Tamilselvan, K.S., et.al.: An IoT enabled smart garbage management system for smart cities – Indian scenario. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 9(4) (2020). ISSN: 2278–3075
Dipak, S., Aithal, S.: Smart city waste management through ICT and IoT driven solution. Int. J. Appl. Eng. Manag. Lett. (IJAEML) 5(1), 51–65 (2021). (ISSN: 2581-7000)
Gaikwad, A.T.: LBP and PCA based on face recognition system, pp. 368–373, November 2018. ISSN 2348-8034
Bhangale, K.B., Jadhav, K.M., Shirke, Y.R.: Robust pose invariant face recognition using DCP and LB. Int. J. Manag. Technol. Eng. 8(9) 1026–1034 (2018). ISSN NO: 2249–7455
Kalaiselvi, P., Nithya, S.: Face recognition system under varying lighting conditions. IOSR J. Comput. Eng. 14(3), 79–88 (2013). (e-ISSN: 2278-0661, p-ISSN: 2278-8727)
Cai, Y., Xu, G., Li, A., Wang, X.: A novel improved local binary pattern and its application to the fault diagnosis of diesel engine. Shock Vib. 2020, 15 (2020). https://doi.org/10.1155/2020/9830162
Ding, C., Choi, J., Tao, D., Davis, L.S.: Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 38(3), 518–531 (2016). PDF: Robust Pose Invariant Face Recognition using DCP and LBP
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. JMLR 12, 2825–2830 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sivaranjani, P., Gowri, P., Murugan, B.M.R., Suresh, E., Janarthanan, A. (2022). A Smart Garbage System for Smart Cities Using Digital Image Processing. In: Chen, J.IZ., Tavares, J.M.R.S., Shi, F. (eds) Third International Conference on Image Processing and Capsule Networks. ICIPCN 2022. Lecture Notes in Networks and Systems, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-031-12413-6_60
Download citation
DOI: https://doi.org/10.1007/978-3-031-12413-6_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12412-9
Online ISBN: 978-3-031-12413-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)