Real-time waste classification modeling for embedded systems based on deep convolutional neural networks

Name: Sorawit Thokrairak

LCSH: Burapha University -- Data Science

Classification :.DDC: 006.32

LCSH: Neural networks (Computer science)

LCSH: Artificial intelligence

LCSH: Refuse and refuse disposal

LCSH: Embedded computer systems

LCSH: Waste classification

Abstract: Machine Learning technology grows in the field of automatic waste sorting machines equipped with an intelligent unit. This intelligent unit runs on an embedded system that mostly has lower computation power (both CPU and GPU) and lower RAM. However, to archive a higher accuracy rate on classification one has to use a sophisticated classification AI model, which needs less computational power. We considered our experiment using 3 pre-trained models based on COCO dataset, namely the ssd_mobilenet_v2_coco, the ssd_inception_v2_coco, and the ssd_resnet_50_fpn_coco due to their good quality. The results show that although the AI model based on the ssd_resnet_50_fpn_coco has the highest accuracy (99.75%), it consumes the most computational power. In contrast, the one based on the ssd_mobilenet_v2_coco has acceptable accuracy (98.83%) and it consumes the lowest computational power. We decided that the most suitable AI model for embedded systems is the one that is trained with the pre-trained ssd_mobilenet_v2_coco model.

Burapha University. Library

Address: CHONBURI

Email: buulibrary@buu.ac.th

Name: Prajaks Jitngernmadan

Role: Principal advisor

Created: 2022

Modified: 2023-05-11

Issued: 2023-05-11

วิทยานิพนธ์/Thesis

application/pdf

CallNumber: Th 006.32 S713R

eng

DegreeName: Master of Science

Level: Master's Degree

Descipline: Data Science

Grantor: Burapha University

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