Optimizing Waste Management with Advanced Object Detection for Garbage Classification
This addresses waste management challenges for environmental applications, but it is incremental as it applies an existing method (YOLO V5) to a new domain (garbage classification).
The paper tackles the problem of inefficient waste management by implementing an AI-based object detection system using YOLO V5 to classify trash types such as plastic, paper, glass, metal, cardboard, and biodegradables, demonstrating its effectiveness in identifying waste.
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.