2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 12 , Issue 2 , PP: 65-74, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Sandeep Kumar 1 , Vikrant Shokeen 2 , Amit Sharma 3 , Prabhat K. Srivastava 4 , Upasana Dugal 5 , Aditi Sharma 6 *
Doi: https://doi.org/10.54216/JISIoT.120205
Waste management has been an issue due to low awareness among people of any country to lead major environmental contamination, tragic accidents, and unfavorable working conditions for landfill workers. The Lack of precise and efficient object detection could be a barrier in the growth of computer vision-based systems. As per the latest research articles, pre-trained models could be used for Trash Bin detection in real time and for recommending appropriate actions after detection. Using a unique validation dataset made up of predicted trash items, the two classes of acceptable object identification models, YOLO (You Only Look Once) and SSD (Single Shot Multibox Detector), are then contrasted. It is concluded that SSD performs noticeably better than YOLO in identifying trash objects based on several performance metrics computed utilizing multiple open-source research projects. The model is then built up to recognize several trash object types after being pre-trained using Microsoft's COCO (Common Objects in Context) dataset. Our initiative intends to enhance sustainable waste management, make trash sorting incredibly simple, and guard against serious illnesses and accidents at landfill and garbage disposal sites.
Garbage detection , Machine Learning , Waste management , You Only Look Once Mode , Single Shot Multibox Detector , Common Objects in Context
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