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