IncepX-Ensemble: Performance Enhancement Based on Data Augmentation and Hybrid Learning for Recycling Transparent PET Bottles

IEEE Access

卷:10

Debapriya Hazra¹  Subhajit Chatterjee¹  Yung-Cheol Byun¹ 

[1] Department of Computer Engineering, Jeju National University, Jeju, South Korea;

关键词: Deep learning;    transfer learning;    ensemble learning;    image classification;    recycling;    plastic waste;   

DOI  :  10.1109/ACCESS.2022.3174076

来源: DOAJ

【 摘 要 】

Recycling used plastic bottles is a significant step towards environmental protection and land pollution. Lifestyle changes in developing countries such as South Korea have substantially impacted the increase in the use rate of plastic waste year by year. Plastic bottles of various types usually have varied recycling values. Human labor is used to categorize and handle recyclable waste in most countries manually. This study aims to provide an automated recyclable transparent plastic bottle classification system that can be used to replace existing trash disposal methods. Studies on the usefulness of Transfer Learning (TL) and Ensemble Learning (EL) techniques in image categorization have been conducted recently. At first developed InceptonV3, Xception, ResNet152, and DenseNet169 based TL structure. Then to enhance the level, we have proposed an ensemble model with InceptionV3 and Xception, named IncepX-Ensemble, to classify images in well-manner and poorly transparent plastic bottle images. After that, to evaluate the proposed algorithm, we have applied data augmentation to overcome the imbalanced problem. In our research, the accuracy for predicting transparent plastic bottles value reached 99.76% accuracy. The proposed ensemble model’s potential use and limitations have also has examined. This method provides the image classification of transparent plastic bottles and has essential potential value for environmental protection and pollution control.

【 授权许可】

Unknown