DETECTION WASTE USING YOLOV5
Keywords:
Waste Detection, Waste Classification, Organic Waste, Inorganic Waste, YOLOv5, Smart Waste Management, Artificial Intelligence (AI), Recycling, Waste Sorting, Sustainable Waste Management, Web Technology, Real-Time Object DetectionAbstract
One of the most important factors in city development in the past has been the efficient use of organic and inorganic waste. This research aims to improve detection and classification systems using the YOLOv5 method implemented on a web-based system. This system is designed to increase the efficiency of the recycling process while simplifying the automatic waste separation process. The YOLOv5 algorithm is one of the most effective artificial intelligence (AI) methods for detecting objects in real-time in image data sets with labels for organic and inorganic samples. The results of the system analysis show that the YOLOv5 model has a high level of accuracy in classifying the two types of waste, so it can help the general public in handling waste more quickly and easily. This web-based system also provides clear information regarding the types of waste that need to be discussed, as well as waste optimization at the local level using web-based smart technology. This research is expected to have a positive impact on technology-based waste management, increase public awareness of the importance of effective waste management, accelerate the waste management process, and increase the efficiency of the recycling system.References
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