YOLO (YOU ONLY LOOK ONCE) ALGORITHM-BASED AUTOMATIC WASTE CLASSIFICATION SYSTEM

Authors:

Seba Maity,Tania Chakraborty,Ratnesh Pandey,Hritam Sarkar,

DOI NO:

https://doi.org/10.26782/jmcms.2023.08.00003

Keywords:

Waste management automated system,YOLO algorithm,Computer vision,Image processing, keras,Tensorflow,Dataset,Arduino UNO,Servo motor,

Abstract

Our paper presents the design and implementation of an automated waste management system that utilizes the You Only Look Once (YOLO) algorithm and computer vision techniques for efficient waste sorting. The escalating global concern regarding waste management necessitates the development of automated systems to address the challenges associated with waste sorting. By leveraging YOLO's object detection capabilities and the power of computer vision, our system accurately identifies and classifies various types of waste in real time. The YOLO algorithm's efficiency and speed enable the swift processing of waste items, facilitating efficient sorting into predefined placements. This automated system not only improves accuracy but also reduces health risks for workers and minimizes environmental harm. Complemented by public awareness campaigns promoting proper waste separation and recycling practices, our research contributes to advancing waste management technologies and fostering sustainable practices for a healthier environment.

Refference:

I. Abdul Vahab, Maruti S Naik, Prasanna G Raikar an Prasad S R4,
“Applications of Object Detection System”, International Research Journal of Engineering and Technology (IRJET)
II. Akar, Mehmet, and Ismail Temiz. “Motion controller design for the speed
control of dc servo motor.” International Journal of Applied Mathematics and Informatics 1.4 (2007): 131-137.
III. Aacha Gautam, Anjana Kumari, Pankaj Singh: “The Concept of Object
Recognition”, International Journal of Advanced Research in Computer
Scienceand Software Engineering, Volume 5, Issue 3, March 2015
IV. Banzi, Massimo, and Michael Shiloh. Getting started with Arduino. Maker
Media, Inc., 2022.
V. D. Hoornweg and P. Bhada-Tata, “A Global Review of Solid Waste Management,” (2012) 1-116.
VI. Geethapriya S, N. Duraimurugan, S.P. Chokkalingam, “Real-Time Object Detection with Yolo”, International Journal of Engineering and Advanced Technology (IJEAT)
VII. Hammad Naeem, Jawad Ahmad and Muhammad Tayyab, “Real-Time Object Detection and Tracking”, IEEE
VIII. https://img.livestrong.com/630x/photos.demandstudios.com/getty/article/ 228/54/178229012.jpg

IX. https://imageds.wisegeek.com/black-webcam.jpg
X. Real-Time Object Detection”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779- 788
XI. Meera M K, & Shajee Mohan B S. 2016, “Object recognition in images”, International Conference on Information 60 Science (ICIS).
XII. Niehs.nih.gov, “Cancer and the Environment,” 46, 2018. [Online].
XIII. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie & Laith Farhan Journal of Big Data volume 8, Article number: 53 (2021)
XIV. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie & Laith Farhan Journal of Big Data volume 8, Article number: 53 (2021)
XV. V. Gajjar, A. Gurnani and Y. Khandhediya, “Human Detection and Tracking for Video Surveillance: A Cognitive Science Approach,” in 2017 IEEE International Conference on Computer Vision Workshops, 2017
XVI. You Only Look Once: Unified, Real-Time Object Detection. Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi,

View Download