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@article{193881,
        author = {Raksha Padalwar and Pornima Bhurle and Harsh Batule and Piyush Chafle and Gaurav Rinayat and Dr. Amol Gaikwad},
        title = {Biomedical Waste Identification Using Yolov8},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {2288-2294},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193881},
        abstract = {Biomedical waste (BMW) management is one of the biggest challenges in healthcare. The volume of waste generated is growing, which increases the risks of infection and environmental pollution. Traditional waste segregation methods rely a lot on manual work. This ap- proach is slow, prone to mistakes, and can be dangerous for workers. To solve this problem, this research introduces a real-time biomedical waste identification and classification system using YOLOv8, along with an integrated hardware-assisted bin-indication mechanism. The proposed model has been trained on a custom multi-class biomedical waste dataset comprising 12 categories within a clinical environment. The system achieves high accuracy in object detection and maps each detected item to its corresponding CPCB-compliant bin category. The hardware unit comprised Arduino-based LED indicators for immediate disposal guidance. Experimental results have clearly shown enhanced performance after the re-optimization process with higher precisions, recalls, and mAP values of the model. This system is designed to minimize human error in waste segregation and strive for safer and more efficient handling of biomedical waste.},
        keywords = {Biomedical Waste, YOLOv8, Object Detection, Deep Learning, Image Processing, Waste Classification, Ar- duino Integration, Smart Segregation System.},
        month = {March},
        }