A lightweight multiscale convolutional neural network for garbage sorting

Abstract

Waste sorting plays a vital role in establishing a sustainable society by effectively reducing resource waste and promoting its recycling. However, traditional garbage sorting heavily relies on manual labor, which is inefficient, costly, and constrained by limited human resources. To address these challenges, this paper employs the convolutional neural network technique in deep learning for intelligent waste sorting. Firstly, a multi-scale processing strategy is introduced to enhance the system's resilience and accuracy by considering feature information at various scales. Secondly, a lightweight approach using tiny convolutions instead of large convolutions is adopted to reduce model parameters. Combining the advantages of both, we constructed a lightweight multiscale convolution (LMConv) and experiments the Lightweight Multiscale Convolutional Neural Network (LMNet) based on LMConv, and its optimal convolutional architecture is determined through ablation experiments. The experiment results demonstrate that LMNet outperforms other well-known convolutional neural network models in the area of garbage sorting.