Explainable CNN-Based Multiclass Household Waste Classification Using Grad-CAM for Smart Waste Management

Abstract

Automated waste classification using computer vision has become essential for improving environmental sustainability and reducing manual sorting effort. This study presents an enhanced waste image classification model based on EfficientNet-B0, trained using a two-stage transfer learning strategy that combines feature extraction and fine-tuning. The proposed approach aims to enhance classification accuracy while maintaining computational efficiency. Experimental evaluations conducted on a heterogeneous multi-class waste dataset demonstrate the superiority of the proposed method. The confusion matrix results indicate a high proportion of correct predictions across most categories, with only minor misclassifications among visually similar classes, such as metal and paper. The model's robustness is further validated through 5-Fold Cross-Validation, which yields an average accuracy of 94.3% with a standard deviation of ±0.007, confirming consistent performance across data partitions. Compared with state-of-the-art CNN architectures, including ResNet50 and DenseNet121, the proposed model achieves the highest accuracy while using the fewest parameters (4.38M), making it suitable for deployment in resource-constrained environments. Additionally, qualitative analysis using Grad-CAM confirms that the model’s decisions are explainable and based on relevant object features. These findings demonstrate that the proposed EfficientNet-B0 model constitutes a reliable, efficient, and interpretable solution for automated waste classification. The model is further evaluated using cross-validation and explainable AI (Grad-CAM) to assess both performance stability and interpretability.