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Smart Trash Segregator Using Deep Learning on Embedded Platform

Publication Type : Conference Paper

Publisher : EAI/Springer Innovations in Communication and Computing

Source : In: Haldorai, A., Ramu, A., Mohanram, S., Chen, MY. (eds) 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-47560-4_34

Url : https://doi.org/10.1007/978-3-030-47560-4_34

ISBN : 9783030475604

Campus : Bengaluru

School : Department of Computer Science and Engineering, School of Engineering

Department : Computer Science

Year : 2021

Abstract : An alarming rate of the rising population of India produces mushrooming waste day by day. Collected waste may not be in segregated form, so most of the waste ends up in landfills without getting potential waste recycled. Traditional waste management systems are deficient in the segregation of waste. So, proposing a Smart Trash Segregator (STS) at source (offices, airport, railway station, bus stop, malls) level. Autonomous Smart Trash Segregator (STS) segregates the trash into six types of waste such as plastic, organic, paper, cardboard, metal, and glass. The system captures the trash image and then classified by a trained deep neural network ported on an embedded platform (Raspberry Pi) and sensor module. Finally, the mechanical actuators interfaced with Raspberry Pi takes the action to drop the classified trash to the corresponding trash bin. The system is trained and tested with a sufficient trash image dataset which achieves the accuracy of 85–96% while performing the complete segregation process. Hence the system attains waste segregation autonomously and efficiently as compared to the traditional segregation system. The system is useful in the perspective of recycling and eventually for sustainable waste management.

Cite this Research Publication : Mahakalkar, N.A., D., R. (2021). Smart Trash Segregator Using Deep Learning on Embedded Platform. In: Haldorai, A., Ramu, A., Mohanram, S., Chen, MY. (eds) 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-47560-4_34

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