Kho, Dickson Yik Cheng and Norazlianie, Sazali and Kettner, Maurice and Friedrich, Christian and Schempp, Constantin and Salim, Naqib and Ismayuzri, Ishak and Saiful Anwar, Che Ghani (2026) Synthetic image data generation via rendering techniques for training AI-based instance segmentation. Journal of Advanced Research in Applied Sciences and Engineering Technology, 62 (1). pp. 158-169. ISSN 2462-1943. (In Press / Online First) (In Press / Online First)
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Synthetic Image Data Generation via Rendering Techniques for Training AI-Based Instance Segmentation.pdf Available under License Creative Commons Attribution Non-commercial. Download (3MB) | Preview |
Abstract
Synthetic image data generation has gained popularity in computer vision and machine learning in recent years. The work introduces a technique for creating artificial image data by utilizing 3D files and rendering methods in Python and Blender. The technique employs BlenderProc, a rendering tool for generating artificial images, to efficiently create a substantial amount of data. The output of the method is saved in JSON format, containing COCO annotations of objects in the images, facilitating seamless integration with current machine-learning pipelines. The paper shows that the created synthetic data can be used to enhance object data during simulation. The method can enhance the accuracy and robustness of machine-learning models by modifying simulation parameters like lighting, camera position, and object orientation to create a variety of images. This is especially beneficial for applications that require significant amounts of labelled real-world data, which can be time-consuming and labour-intensive to obtain. The study addresses the constraints and potential prejudices of creating synthetic data, emphasizing the significance of verifying and assessing the generated data prior to its utilization in machine learning models. Synthetic data generation can be a valuable tool for improving the efficiency and effectiveness of machine learning and computer vision applications. However, it is crucial to thoroughly assess the potential limitations and biases of the generated data. This paper emphasizes the potential of synthetic data generation to enhance the accuracy and resilience of machine learning models, especially in scenarios with limited access to labelled real-world data. This paper introduces a method that efficiently produces substantial amounts of synthetic image data with COCO annotations, serving as a valuable resource for professionals in computer vision and machine learning.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Synthetic image data generation, Rendering techniques, BlenderProc, COCO annotations |
| Subjects: | T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures |
| Faculty/Division: | Faculty of Manufacturing and Mechatronic Engineering Technology Faculty of Mechanical and Automotive Engineering Technology Centre for Research in Advanced Fluid & Processes (Fluid Centre) |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 16 Oct 2024 04:10 |
| Last Modified: | 16 Oct 2024 05:48 |
| URI: | http://umpir.ump.edu.my/id/eprint/42815 |
| Download Statistic: | View Download Statistics |
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