Advanced YOLO-Based Trash Classification and Recycling Assistant for Enhanced Waste Management and Sustainability
M. Guru Vimal Kumar, Madde Kumar, K N S S Chalapathi Rao, P Syamala Rao, Arepalli Tirumala, Eswar Patnala
Abstract
The ever-growing global population has heightened resource consumption and waste generation, emphasizing the quick need for effective waste management to safeguard the environment. Unfortunately, the recycling industry grapples with persistent challenges, primarily in accurate trash classification, a critical factor for successful recycling. Manual sorting, often prone to errors due to subjective human judgment, hampers the recycling process, contributing to inefficiencies. Furthermore, the inherent risks associated with direct contact during the sorting of hazardous materials pose serious health concerns for the workers involved. In response to these challenges, we propose a revolutionary solution: the Trash Classification and Recycling Assistant utilizing YOLO variants V5-V7. This system, rooted in image classification techniques, seeks to elevate the precision of trash sorting. Notably, YOLO variant V7 emerges as the frontrunner, showcasing remarkable accuracy improvements. By utilizing the capabilities of advanced technology, this innovative approach not only streamlines waste sorting processes but also mitigates health risks linked to manual handling of toxic materials. The integration of YOLO variants V5-V7 represents a pivotal step towards ushering in a new era of efficiency and accuracy in recycling practices, thus significantly contributing to the overarching goal of environmental sustainability.