Plastic Waste Classification Using Deep Learning: Insights from the WaDaBa Dataset

TL;DR

This paper demonstrates the effectiveness of deep learning, especially YOLO models, in classifying plastic waste with high accuracy using the WaDaBa dataset, offering scalable solutions for waste management.

Contribution

It evaluates various deep learning models, highlighting YOLO's superior performance for plastic waste classification and providing insights into balancing accuracy and efficiency.

Findings

YOLO-11m achieved 98.03% accuracy and 0.990 mAP50.

MobileNet V2 achieved 97.12% accuracy but was less effective in object detection.

Lightweight models trained faster but with lower accuracy.

Abstract

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.