Plastic Waste Classification Using Deep Learning: Insights from the WaDaBa Dataset
This addresses plastic waste management for recycling applications, but it is incremental as it applies existing deep learning methods to a new dataset.
This study tackled plastic waste classification using deep learning models on the WaDaBa dataset, finding that YOLO-11m achieved the highest accuracy of 98.03% and mAP50 of 0.990, with YOLO models overall proving most effective.
With the increasing use of plastic, the challenges associated with managing plastic waste have become more challenging, emphasizing the need of effective solutions for classification and recycling. This study explores the potential of deep learning, focusing on convolutional neural networks (CNNs) and object detection models like YOLO (You Only Look Once), to tackle this issue using the WaDaBa dataset. The study shows that YOLO- 11m achieved highest accuracy (98.03%) and mAP50 (0.990), with YOLO-11n performing similarly but highest mAP50(0.992). Lightweight models like YOLO-10n trained faster but with lower accuracy, whereas MobileNet V2 showed impressive performance (97.12% accuracy) but fell short in object detection. Our study highlights the potential of deep learning models in transforming how we classify plastic waste, with YOLO models proving to be the most effective. By balancing accuracy and computational efficiency, these models can help to create scalable, impactful solutions in waste management and recycling.