Paper Title
Plastic Waste Detection using YOLOv5 Deep Learning Object Detection Algorithm
Article Identifiers
: Authors
Keywords
Deep Learning,YOLOv5,Object Detection Algorithm
Abstract
Studies indicate that the largest contributor to pollution is discarded plastic trash, which is one of the most worrying environmental concerns. Wildlife along the coast, the ecology, the stability of the ecosystem, and local economies are all at risk due to these plastics. This would inevitably have an impact on marine life as well as human life. The most popular techniques for detecting and measuring plastics have several drawbacks despite being effective. As a result, it's critical to embrace alternative techniques that make use of cutting-edge technology and make it simple for us to recognise and remove plastics. For the purpose of locating and classifying the plastics, we examined the YOLO v5 deep learning object identification methods in this research. The datasets are made using plastic photographs that can be found online. The dataset's image count can be increased with the aid of image augmentation. The performance of the algorithm is examined, and the results are drawn with an explanation of the Mean Average Precision of YOLO v5.
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How To Cite (APA)
Kayala Lakshmi Swetha, Kadiyala Kushala, Kanuri Yogambika Varshini, Karella Baby Jahnavi, & Dr.K.Soumya (March-2023). Plastic Waste Detection using YOLOv5 Deep Learning Object Detection Algorithm. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), b10-b16. https://ijnrd.org/papers/IJNRD2303103.pdf
Issue
Volume 8 Issue 3, March-2023
Pages : b10-b16
Other Publication Details
Paper Reg. ID: IJNRD_188461
Published Paper Id: IJNRD2303103
Downloads: 000122257
Research Area: Computer EngineeringÂ
Author Type: Indian Author
Country: Visakhapatnam, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2303103.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303103
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