Détail du document
Identifiant
oai:arXiv.org:2410.09975
Sujet
Computer Science - Computer Vision...Auteur
Kuang, Everest Z. Bhandari, Kushal Raj Gao, JianxiCatégorie
Computer Science
Année
2024
Date de référencement
23/10/2024
Mots clés
wasteRésumé
Garbage production and littering are persistent global issues that pose significant environmental challenges.
Despite large-scale efforts to manage waste through collection and sorting, existing approaches remain inefficient, leading to inadequate recycling and disposal.
Therefore, developing advanced AI-based systems is less labor intensive approach for addressing the growing waste problem more effectively.
These models can be applied to sorting systems or possibly waste collection robots that may produced in the future.
AI models have grown significantly at identifying objects through object detection.
This paper reviews the implementation of AI models for classifying trash through object detection, specifically focusing on using YOLO V5 for training and testing.
The study demonstrates how YOLO V5 can effectively identify various types of waste, including plastic, paper, glass, metal, cardboard, and biodegradables.
;Comment: 8 pages, 8 figures
Kuang, Everest Z.,Bhandari, Kushal Raj,Gao, Jianxi, 2024, Optimizing Waste Management with Advanced Object Detection for Garbage Classification
Prodromal Parkinson disease signs are predicted by a whole-blood inflammatory transcriptional signature in young Pink1(−/−) rats
−/− data rats expression pink1 disease blood gene
