What question did this study set out to answer?

This research aims to enhance waste classification through deep learning models using hybrid approaches.

March 17, 2026Open Access

A Hybrid CNN Approach in Waste Classification

Key Points

This research aims to enhance waste classification through deep learning models using hybrid approaches.
Investigated multiple convolutional neural networks: DenseNet121, InceptionV3, MobileNetV2, and VGG16.
Utilized data augmentation and transfer learning techniques to improve model performance.
Implemented a hybrid model aggregating features from the four networks.
Evaluated performance using 5-fold cross-validation on two datasets.
The hybrid model achieved a test accuracy of 85.88% on the Garbage Classification dataset.
A test accuracy of 91.26% was noted on the Garbage-Dataset.
Performance metrics included high values for accuracy, precision, recall, F1-score, and ROC-AUC.

Abstract

Recycling plays a vital role in environmental sustainability and the conservation of natural resources. However, increasing population, industrialization, and waste have increased the need for automation in waste sorting processes. This study investigated the performance of deep learning-based classification models on waste classification using two different datasets (Garbage Classification and Garbage-Dataset). Experiments were conducted on four Convolutional Neural Networks (DenseNet121, InceptionV3, MobileNetV2, and VGG16), utilizing data augmentation and transfer learning techniques. A hybrid model was created by aggregating the features of these four models. Performance evaluation was performed with the 5-fold cross-validation method. In both datasets analyzed according to the experimental results, the hybrid model yielded the highest performance metrics, including Accuracy, Precision, Recall, F1-score, and ROC-AUC. A test accuracy rate of 85.88% was obtained in the Garbage Classification dataset and 91.26% in the Garbage-Dataset. The study highlights the critical impact of dataset size and model architecture choices on classification performance, providing an essential foundation for developing automation solutions in waste management.

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