A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 13
Issue 1
IEEE/CAA Journal of Automatica Sinica
| Citation: | H. Pham, A. Le, P. Malvido-Fresnillo, S. Vasudevan, and J. Martínez Lastra, “Efficient dataset generation for stacked meat products instance segmentation in food automation,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 1, pp. 224–226, Jan. 2026. doi: 10.1109/JAS.2025.125798
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S. Vasudevan, M. Lamine Mekhalfi, C. Blanes, M. Lecca, F. Poiesi, P. Ian Chippendale, P. Malvido Fresnillo, W. M. Mohammed, and J. L. Martinez Lastra, “Machine vision and robotics for primary food manipulation and packaging: A survey,” IEEE Access, vol. 12, pp. 152579–152613, 2024.
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K. Bergthold, “GoogleImageScraper,” 2023. [Online]. Available: https://pypi.org/project/GoogleImageScraper/. Accessed on: Dec. 30, 2025.
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SerpApi, LLC., “SerpApi: Google Search API,” 2017. [Online]. Available: https://serpapi.com/. Accessed on: Dec. 30, 2025.
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OpenCV Developers, “OpenCV: Image segmentation,” 2025. [Online]. Available: https://docs.opencv.org/4.x/d3/d47/group__imgproc__segmentation.html. Accessed on: Dec. 30, 2025.
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, and R. Girshick, “Segment anything,” arXiv preprint arXiv: 2304.02643, 2023.
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CVAT.ai, “CVAT: Computer vision annotation tool,” 2024. [Online]. Available: https://www.cvat.ai. Accessed on: Dec. 30, 2025.
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H. Zheng, L. Yang, J. Chen, J. Han, Y. Zhang, P. Liang, Z. Zhao, C. Wang, and D. Z. Chen, “Biomedical image segmentation via representative annotation,” Proc. AAAI Conf. Artificial Intelligence, vol. 33, no. 1, pp. 5901–5908, Jul. 2019,
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Z. Shi, Y. Yang, T. M. Hospedales, and T. Xiang, “Weakly-supervised image annotation and segmentation with objects and attributes,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2525–2538, 2017.
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A. Bauer, S. Trapp, M. Stenger, R. Leppich, S. Kounev, M. Leznik, K. Chard, and I. Foster, “Comprehensive exploration of synthetic data generation: A survey,” arXiv preprint arXiv: 2401.02524, 2024.
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C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” J. Big Data, vol. 6, no. 1, p. 60, Jul. 2019.
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P. Malvido Fresnillo, W. M. Mohammed, S. Vasudevan, J. A. PerezGarcia, and J. L. MartinezLastra, “Generation of realistic synthetic cable images to train deep learning segmentation models,” Machine Vision and Applications, vol. 35, no. 4, p. 84, Jun. 2024.
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R. Barth, J. IJsselmuiden, J. Hemming, and E. V. Henten, “Data synthesis methods for semantic segmentation in agriculture: A capsicum annuum dataset,” Computers and Electronics in Agriculture, vol. 144, pp. 284–296, Dec. 2017.
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Blender Foundation. Blender: Free and Open Source 3D Creation Suite. [Online]. Available: https://www.blender.org/. Accessed on: Dec. 30, 2025.
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Epic Games, Inc., “Unreal engine: Real-time 3D creation platform,” 2023. [Online]. Available: https://www.unrealengine.com. Accessed on: Dec. 30, 2025.
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Polycam, Inc., “Polycam: Cross-platform 3D scanning and mapping,” 2024. [Online]. Available: https://poly.cam/. Accessed on: Dec. 30, 2025.
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G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics,” 2023. [Online]. Available: https://github.com/ultralytics/ultralytics. Accessed on: Dec., 30, 2025.
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Figures(4) / Tables(1)
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