Self-supervised learning-based waste classification model

Xiaoyu Yang; Caifeng Zhou

doi:10.1117/12.2684730

21 July 2023 Self-supervised learning-based waste classification model

Xiaoyu Yang, Caifeng Zhou

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 1271713 (2023) https://doi.org/10.1117/12.2684730
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

With the improvement of people's living standards, waste production is rapidly rising, which has a substantial negative impact on human health, the environment, and economic development. Traditional garbage sorting process relies on manual sorting, which is time-consuming and ineffective. To help people quickly sort the garbage in their lives and reduce the phenomenon of misclassification and non-classification of waste in their lives, a new garbage sorting model is proposed in this paper. This model combines momentum contrast learning and a pretext task for self-supervised learning, and the trained deep neural network is used for a downstream task to classify the supervised spam image data. The combination of contrast learning and the pretext task forces the model to learn deep semantic features in the images, which enables the model to have better generalization ability, improves the robustness of the model, and achieves efficient classification of garbage. The final classification accuracy on the garbage dataset is 89.375% after finetuning the pre-trained deep neural network model, which is 1.472% better than the garbage classification accuracy with supervised learning.

Citation Download Citation

Xiaoyu Yang and Caifeng Zhou "Self-supervised learning-based waste classification model", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 1271713 (21 July 2023); https://doi.org/10.1117/12.2684730

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