Optimizing Waste Management with Squeeze-and-Excitation and Convolutional Block Attention Integration in ResNet-Based Deep Learning Frameworks

Effective waste classification is crucial for sustainable waste management, yet existing automated models face challenges such as misclassification of visually similar waste materials, dataset imbalance, and poor generalization to real-world conditions. This study addresses these limitations by integrating Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) mechanisms into ResNet-50, a deep learning model, to improve waste classification accuracy. Utilizing the TrashNet dataset (six waste categories), we have employed the Synthetic Minority Over-sampling Technique (SMOTE) to balance the dataset, increasing samples from 2,527 to 3,564. Experimental results demonstrate significant improvements: ResNet-50 achieves 74.42% test accuracy, SE+ResNet-50 improves accuracy to 93.47%, and CBAM+ResNet-50 reaches 95.74%. The findings highlight that attention-based deep learning models can significantly enhance feature extraction, improve classification accuracy, and optimize waste segregation processes, contributing to more efficient recycling and waste management automation. Future work includes extending the model to classify hazardous and mixed waste, ensuring broader applicability in real-world waste management systems.