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@article{185371,
        author = {Kamya Rajesh Karde and Poonam Rajesh Sonawane},
        title = {Real-time Garbage Detection and Monitoring System using Image Processing and Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {1065-1069},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185371},
        abstract = {The rapid growth of waste generation has raised critical environmental and public health challenges, emphasizing the need for intelligent and automated management solutions. Manual waste collection and monitoring are inefficient, error-prone and labor-intensive, making automation essential for sustainable practices. This research proposes a Real-Time Waste Detection and Monitoring System that combines image processing and deep learning to identify, classify and manage waste. The system employs a YOLOv8-based detection framework to recognize multiple waste categories and integrates automated collection mechanisms for efficient disposal. Real-time data on waste type, volume and location are transmitted to a centralized web platform, enabling continuous monitoring, analysis and decision-making. By uniting detection, collection and real-time web reporting, the proposed system improves waste segregation and timely collection which further contributes to sustainable recycling practices while reducing landfill dependency and environmental pollution.},
        keywords = {Real-time garbage detection, garbage collection, YOLOv8, deep learning, computer vision, image processing, IoT monitoring, smart waste management},
        month = {October},
        }