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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | AI Driven Real Time Waste Segregation Using Computer Vision |
| Authors Name: | Sahil Yadav , Sahil Suman , Rahul Kumar , Pratyush Yadav |
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IJRTI_204611
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| Published Paper Id: | IJRTI2506051 |
| Published In: | Volume 10 Issue 6, June-2025 |
| DOI: | |
| Abstract: | Efficient and scalable waste management has become imperative in mitigating environmental degradation and promoting sustainable urban ecosystems. Manual waste segregation is labor-intensive, error-prone, and insufficient to address the increasing volume of heterogeneous waste generated in modern cities. This paper presents the design and implementation of an AI-driven, real-time waste segregation system utilizing computer vision and deep learning for automated waste classification. The proposed system employs the YOLOv8 object detection model, optimized for edge devices to achieve low-latency, high-accuracy performance in dynamic, real-world environments. A custom dataset supplemented with publicly available TrashNet and TACO datasets was compiled, encompassing region-specific waste images under diverse lighting and background conditions. The system architecture integrates image preprocessing via OpenCV, classification through YOLOv8, and data persistence using MongoDB for scalable and flexible data management. Real-time classification identifies waste into four categories: biodegradable, non-biodegradable, recyclable, and hazardous, with the final model achieving a mean average precision (mAP) of 95% at IoU threshold 0.5. The modular system design ensures scalability, reliability, and high classification accuracy, supporting integration with IoT frameworks for enhanced waste monitoring and automated bin activation. Extensive unit and integration testing validated system robustness, achieving sub-2-second end-to-end latency and consistent database logging. The study highlights the system’s potential for deployment in smart cities, industrial zones, and waste management facilities, contributing to improved sustainability practices and regulatory compliance. Future work will explore multimodal data integration and robotic actuation for complete automated waste handling systems. |
| Keywords: | AI-driven waste management, real-time waste segregation, computer vision, YOLOv8, object detection, deep learning, OpenCV, MongoDB, smart city applications, sustainable waste handling, automated waste classification, edge computing, IoT-based waste monitoring, image processing. |
| Cite Article: | "AI Driven Real Time Waste Segregation Using Computer Vision", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.a463-a467, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506051.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2506051
Registration ID:204611
Published In: Volume 10 Issue 6, June-2025
DOI (Digital Object Identifier):
Page No: a463-a467 Country: Gurugram, Haryana, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2506051 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2506051 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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