General waste is commonly managed to reduce pollution. Similarly, medical waste can be classified and managed to not only reduce pollution but also mitigate health risks and accidental injuries. Medical waste includes a variety of materials such as those contaminated with body fluids, sharps waste, and chemical waste. This study evaluates modern Artificial Intelligence methods for classifying medical waste such as facemasks, gloves, and syringes. Various classification models, including CNN, ResNet50, YOLO v3, and YOLO v4, were used and compared. YOLO v4 achieves a higher mAP (89.21%), surpassing YOLO v3 and other YOLO models used in waste classification studies. YOLO v4 was then tested in object detection and successfully identified masks, gloves, and syringes. Further performance evaluations are necessary to enhance the detection of medical waste and other objects in various applications.


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    Titel :

    Medical Waste Detection and Classification Through YOLO Algorithms


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Abdul Majeed, Anwar P.P. (Herausgeber:in) / Yap, Eng Hwa (Herausgeber:in) / Liu, Pengcheng (Herausgeber:in) / Huang, Xiaowei (Herausgeber:in) / Nguyen, Anh (Herausgeber:in) / Chen, Wei (Herausgeber:in) / Kim, Ue-Hwan (Herausgeber:in) / Moktar, Muhammad H. Bin (Autor:in) / Hajjaj, Sami Salama Hussen (Autor:in) / Mohamed, Hassan (Autor:in)

    Kongress:

    International Conference on Robot Intelligence Technology and Applications ; 2023 ; Taicang December 06, 2023 - December 08, 2023



    Erscheinungsdatum :

    29.11.2024


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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