Abstract
In this paper, ConvNeXt is selected as a model for waste classification from digital images. ConvNeXt is a CNN-based backbone network that has been proposed to further improve the performance of models for visual tasks, following the various types of research work that have been generated based on Transformer. In this paper, we take ConvNeXt as the backbone to obtain an efficient waste classification model. In our experiments, we categorized waste into four classes based on predefined classification criteria. We have collected 1,660 labeled images for model training. By using ConvNeXt, we observed that the best experimental result in this paper was from ConvNeXt, which has achieved an accuracy 79.88% in the waste classification. In order to evaluate the model, we consider AP and mAP for waste classification. Our experimental results show that using Mask R-CNN network with ConvNeXt as the backbone outperforms the existing methods for waste classification.
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Qi, J., Nguyen, M., Yan, W.Q. (2023). Waste Classification from Digital Images Using ConvNeXt. In: Wang, H., et al. Image and Video Technology. PSIVT 2022. Lecture Notes in Computer Science, vol 13763. Springer, Cham. https://doi.org/10.1007/978-3-031-26431-3_1
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