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@article{174450,
        author = {Govind Kela and Aniket Kesarwani and Advait Khaire and Reena Sonkusare},
        title = {Ecowatch - Machine Learning Based Litter Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4594-4597},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174450},
        abstract = {Urban environments face significant challenges due to litter accumulation, adversely affecting public health, wildlife, and overall environmental sustainability. Existing manual detection and cleanup methods are inefficient, prompting the need for automated solutions. This paper presents EcoWatch, a real-time litter detection system leveraging advanced machine learning models—YOLOv8 and Faster R-CNN—to identify and classify litter from moving vehicles. By utilizing the TACO dataset, EcoWatch demonstrates its ability to accurately detect various litter types, including plastic bottles, aluminum cans, glass bottles, and food wrappers, under diverse environmental conditions.},
        keywords = {},
        month = {March},
        }