Advances in Garbage Detection and Classification: A Comprehensive Study of Computer Vision Algorithms
Keywords:
Computer Vision, Garbage Detection, Object Detection, Single-Shot Learning, Transfer Learning, Waste Classification, Waste Detection, YOLOV4Abstract
Effective waste detection and classification are crucial for addressing waste management challenges and promoting recycling and reuse of waste materials. The long-term environmental impacts of plastic, metal, and glass-based waste highlight the importance of proper identification, sorting, and utilization of these waste categories. Although various deep learning algorithms have been developed for waste detection, they often struggle to detect multiple garbage categories from a single input image. This research focuses on utilizing computer vision algorithms, specifically the YOLO (You Only Look Once) approach and its variant, which incorporates Convolutional Neural Network (CNN) models, for garbage detection and classification. The efficacy of these models is demonstrated through their impressive performance in waste management tasks. In summary, this research underscores the prowess of Tiny YOLOv4, not only amplifying waste detection capabilities but also envisioning its transformative role in advancing automated waste management practices.
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