Comparing modified Yolo V5 and Faster Regional Convolutional Neural Network performance for Recycle Waste Classification

Waste management research is becoming well-established all over the world. However, there are still improvements needed for developing countries in increasing the effectiveness of waste management. Effective waste management for developing countries is needed to reduce the environmental issues, whic...

Full description

Bibliographic Details
Published in:2024 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2024 - Proceedings
Main Author: Hamzah R.; Ang L.; Roslan R.; Ibrahim Teo N.H.; Abdul Samad K.; Fariza Abu Samah K.A.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203848725&doi=10.1109%2fI2CACIS61270.2024.10649835&partnerID=40&md5=97e7e101570105c0056568f670651667
id 2-s2.0-85203848725
spelling 2-s2.0-85203848725
Hamzah R.; Ang L.; Roslan R.; Ibrahim Teo N.H.; Abdul Samad K.; Fariza Abu Samah K.A.
Comparing modified Yolo V5 and Faster Regional Convolutional Neural Network performance for Recycle Waste Classification
2024
2024 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2024 - Proceedings


10.1109/I2CACIS61270.2024.10649835
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203848725&doi=10.1109%2fI2CACIS61270.2024.10649835&partnerID=40&md5=97e7e101570105c0056568f670651667
Waste management research is becoming well-established all over the world. However, there are still improvements needed for developing countries in increasing the effectiveness of waste management. Effective waste management for developing countries is needed to reduce the environmental issues, which have a significant impact. An issue arising from the rise in insect population and diversity of pests is a digestive system problem. Insufficient recycling and waste management practices can have detrimental effects on economic development, resulting in air pollution and health issues. Implementing computer technology such as object recognition has the potential to be advantageous in the field of waste management. Deep learning is now the most widely used approach for object detection. We propose the integration of new modules of Coordinate Attention (CA) mechanism module, K-means++ algorithm and Cascade Shuffle Space to Depth in the Yolo Version 5 to improve the accuracy of the recognition performance. Through the experiments and comparison, the modified version of Yolo v5 perform better performance compared to conventional Yolo V5 and Faster RCNN. © 2024 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Hamzah R.; Ang L.; Roslan R.; Ibrahim Teo N.H.; Abdul Samad K.; Fariza Abu Samah K.A.
spellingShingle Hamzah R.; Ang L.; Roslan R.; Ibrahim Teo N.H.; Abdul Samad K.; Fariza Abu Samah K.A.
Comparing modified Yolo V5 and Faster Regional Convolutional Neural Network performance for Recycle Waste Classification
author_facet Hamzah R.; Ang L.; Roslan R.; Ibrahim Teo N.H.; Abdul Samad K.; Fariza Abu Samah K.A.
author_sort Hamzah R.; Ang L.; Roslan R.; Ibrahim Teo N.H.; Abdul Samad K.; Fariza Abu Samah K.A.
title Comparing modified Yolo V5 and Faster Regional Convolutional Neural Network performance for Recycle Waste Classification
title_short Comparing modified Yolo V5 and Faster Regional Convolutional Neural Network performance for Recycle Waste Classification
title_full Comparing modified Yolo V5 and Faster Regional Convolutional Neural Network performance for Recycle Waste Classification
title_fullStr Comparing modified Yolo V5 and Faster Regional Convolutional Neural Network performance for Recycle Waste Classification
title_full_unstemmed Comparing modified Yolo V5 and Faster Regional Convolutional Neural Network performance for Recycle Waste Classification
title_sort Comparing modified Yolo V5 and Faster Regional Convolutional Neural Network performance for Recycle Waste Classification
publishDate 2024
container_title 2024 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2024 - Proceedings
container_volume
container_issue
doi_str_mv 10.1109/I2CACIS61270.2024.10649835
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203848725&doi=10.1109%2fI2CACIS61270.2024.10649835&partnerID=40&md5=97e7e101570105c0056568f670651667
description Waste management research is becoming well-established all over the world. However, there are still improvements needed for developing countries in increasing the effectiveness of waste management. Effective waste management for developing countries is needed to reduce the environmental issues, which have a significant impact. An issue arising from the rise in insect population and diversity of pests is a digestive system problem. Insufficient recycling and waste management practices can have detrimental effects on economic development, resulting in air pollution and health issues. Implementing computer technology such as object recognition has the potential to be advantageous in the field of waste management. Deep learning is now the most widely used approach for object detection. We propose the integration of new modules of Coordinate Attention (CA) mechanism module, K-means++ algorithm and Cascade Shuffle Space to Depth in the Yolo Version 5 to improve the accuracy of the recognition performance. Through the experiments and comparison, the modified version of Yolo v5 perform better performance compared to conventional Yolo V5 and Faster RCNN. © 2024 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
_version_ 1812871795727925248