Development of an AI-Based Waste Segregation System with Real-Time Object Classification and Automated Bin Sorting Mechanism
Keywords:
Artificial Intelligence, Computer Vision, Raspberry Pi, YOLO, Waste ClassificationAbstract
Manual segregation of waste at its point of origin is labor-intensive, error-prone, and a persistent bottleneck in waste management. Advancements in artificial intelligence have produced accurate image-based waste classifiers, but many of these studies stay within controlled laboratory conditions. They rarely integrate classification with a physical sorting mechanism that can operate in practical, small-scale settings. The study proposes the development of an AI-based waste segregation system that combines real-time object classification with an automated bin sorting mechanism. The system will identify common waste materials, categorized as biodegradable, non-biodegradable, and recyclable, through real-time image recognition, then direct each item into its corresponding bin without manual intervention. The development process will focus on a low-cost, practical prototype that keeps classification and sorting delay within a reasonable real-time response. The system will be evaluated for classification accuracy and overall sorting performance under real-world testing conditions. The study aims to reduce human error, processing delays, and exposure to hazardous materials, while giving resource-constrained communities and organizations a feasible tool for cleaner waste streams and more effective recycling.
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Copyright (c) 2026 Karl Christian L. Evia, Christian Jake R. Cano, Rayeli S. Santos, Erill Mico C. Arceo, Jerez I. Cortez (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
