Garbage image classification robotic arm based on deep learning

Shaofan Lyu

doi:10.1117/12.3026363

27 March 2024 Garbage image classification robotic arm based on deep learning

Shaofan Lyu

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310502 (2024) https://doi.org/10.1117/12.3026363
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

This study aims to compare and evaluate the performance of four popular deep learning models (CNN, ResNet50, VGG16, and InceptionResNetV2) in the garbage classification task. Waste classification is essential for sustainable development, and automated waste classification systems have become the key to solving the increasing global waste problem. To achieve accurate waste classification, these systems utilize various machine learning algorithms. In recent years, deep learning models have achieved great success in image classification tasks, so they have attracted much attention in garbage classification. In this study, we comprehensively evaluate the performance of CNN, ResNet50, VGG16, and InceptionResNetV2 models in classifying garbage into 12 different categories by comparing metrics such as accuracy, precision, and confusion matrix. The experimental results show that the InceptionResNetV2 model outperforms other models in the garbage classification task, with high precision and recall. This means that using the InceptionResNetV2 model for garbage classification can identify and separate different types of garbage more accurately. Finally, the deep learning model is connected to the CoppeliaSim platform to control the movement of the robotic arm based on the predictive results of the deep learning model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shaofan Lyu "Garbage image classification robotic arm based on deep learning", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310502 (27 March 2024); https://doi.org/10.1117/12.3026363

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