Simulation-based machine learning training data generation system

Mu Liang; Yu Xu

doi:10.1117/12.2684655

19 October 2023 Simulation-based machine learning training data generation system

Mu Liang, Yu Xu

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270923 (2023) https://doi.org/10.1117/12.2684655
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

In recent years, machine learning has made considerable progress in various fields. The training of deep learning algorithm model requires a large amount of data, and a large amount of data annotation is very costly. In this case, the use of simulation data can provide great convenience. Using the three-dimensional reconstruction algorithm to construct a three-dimensional model of the real scene can speed up the construction of simulation data. The model has problems such as model damage and model fragmentation. The repair model takes a long time and the repair effect is poor. To solve these problems, we designed and developed a simulation training data generation system. The system uses Blender as a development platform, introduces a three-dimensional reconstruction algorithm to initialize the model, transforms the model into a point cloud for repair operation, and proposes a point cloud region adjustment method and a point cloud fast repair method. Finally, the refined model is used to generate a data set with true values. The experimental results show that the system we designed can improve the efficiency and accuracy of data generation and efficiently generate simulation training data for machine learning.

Citation Download Citation

Mu Liang and Yu Xu "Simulation-based machine learning training data generation system", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270923 (19 October 2023); https://doi.org/10.1117/12.2684655

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KEYWORDS

Point clouds

3D modeling

Data modeling

Education and training

RGB color model

Reconstruction algorithms

Modeling

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