Towards Smart Waste Sorting: Lightweight CNN with Attention Mechanisms for Enhanced Classification

Managing the growing challenges of municipal solid waste is crucial for environmental sustainability, and effective waste management plays a key role in achieving that goal. One promising solution is automating the sorting of refuse, which can greatly improve recycling and overall waste management. However, many current deep learning models that rely on transfer learning suffer from high computational demands and limited adaptability in resource-constrained environments. In this work, we introduce a lightweight convolutional neural network (CNN) specifically designed to overcome these limitations in trash classification. The model incorporates advanced attention mechanism, namely Squeeze-and-Excitation (SE) and Spatial Attention (SA) blocks along with the Mish activation function, collectively augmenting feature representation and optimizing gradient flow. Evaluated on the Kaggle Garbage Dataset, our proposed model achieves a very high classification accuracy of 96.54%, far beyond conventional transfer learning models including VGG16, ResNet50, and EfficientNet B0. With just over 0.8 million parameters and an inference time of only 15 milliseconds per image, the model demonstrates both high computational efficiency and excellent scalability. These results highlight the model’s potential for real-time deployment in resource-limited settings, offering a sustainable and effective alternative for automated waste management. Ultimately, our approach promises to improve garbage classification systems and support more environmentally friendly waste management strategies by leveraging more efficient tools.