YOLOv8-Based Model for Solid Waste Detection
Abstract
The primary focus of this article was to employ Ultralytics technology, specifically YOLOv8, in object recognition. This involved utilizing supervised learning and other machine learning techniques. The article took into consideration the definitions of object detection and model training to effectively categorize solid waste, thereby facilitating recycling efforts. Following this, each object class was manually identified using the LabelImg tagger, considering the positions of the objects within the images. This approach led to the analysis of 1517 images and produced notably high-quality and significant results.
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References
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