An integrated deep-learning model for smart waste classification.

Area of Science:

• Computer Science
• Environmental Science
• Artificial Intelligence

Background:

• Efficient waste management is crucial for environmental health and economic stability.
• Rapid urbanization necessitates automated and innovative solutions for waste classification.
• Existing waste classification models face challenges with accuracy and overfitting, especially with limited data.

Purpose of the Study:

• To develop a novel, robust, and highly accurate smart waste classification model.
• To leverage a hybrid deep learning architecture for improved feature extraction and classification.
• To enhance classification performance using data augmentation techniques.

Main Methods:

• Utilized an optimized DenseNet-121 model for advanced feature extraction from the TrashNet dataset.
• Integrated Support Vector Machine (SVM) for precise classification of extracted waste features.
• Implemented data augmentation strategies to improve model generalization and mitigate overfitting.

Main Results:

• The hybrid Optimized DenseNet-121 + SVM model achieved an exceptional classification accuracy of 99.84%.
• The proposed model demonstrated superior performance compared to existing waste classification methods.
• Data augmentation significantly contributed to enhanced accuracy and model robustness.

Conclusions:

• The developed hybrid deep learning model offers a highly effective solution for automated waste classification.
• This approach has the potential to significantly improve waste management practices, contributing to sustainability.
• The model's high accuracy and robustness pave the way for cleaner urban environments.