AttenFusion-Net: An Attention-Enhanced Deep Learning Framework for Automated Municipal Solid Waste Segregation

The growing problem of municipal solid waste (MSW) generation presents a challenge in the efficient and scalable sorting of urban solid waste. Although deep learning models hold potential for automated waste segregation, their effectiveness is limited by the large Intra-class variance, Inter-class Similarity and Class Imbalance in some real world data. To overcome these limitations, we present a deep learning model with attention mechanisms, ResNet-CBAM, combining a ResNet-50 backbone with a Convolutional Block Attention Module (CBAM) to enhance feature representation through channel and spatial awareness. The approach is tested on the open Waste Segregation Image Dataset, which includes eight classes of biodegradable and non-biodegradable waste. To overcome the class imbalance problem, a combination of data augmentation and undersampling is applied. The findings reveal that our approach can attain an accuracy of 93.09% and an F1 score of 0.9122, surpassing some baseline approaches. The use of attention mechanisms improves the model's discriminative ability, resulting in better classification performance, especially in visually diverse environments. While minor errors still remain among visually similar classes, these results show a stable performance of the proposed model for all classes. In conclusion, the ResNet-CBAM model presents a reliable and transparent solution for waste classification, which can be further integrated into smart waste management and smart cities.