Real-Time Assembly Operation Recognition
Keywords:
computer vision, assembly operation, recognition
Abstract
This research is concerned to propose a computer vision algorithm to track manual assembly task. Manual assembly in case of electronics parts are used largely in automotive industry. The phases tracking of assembly could also be used for learning purposes such in case showed in this research, checking the assembly of an electronic educational board. The algorithms used for detection of different components are CNN (Convolutional Neuronal Network) as well as blob detection.
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References
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[2]. Zhang Z., Peng G., Wang W., Chen Y., Jia Y., Liu S., Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model, Sensors, 22, (11), 4279, 2022.
[3]. Wenjin T., Md Al-Amin Haodong C., Ming C. L., Zhaozheng Y., Ruwen Q., Real-Time Assembly Operation Recognition with Fog Computing and Transfer Learning for Human-Centered Intelligent Manufacturing, Procedia Manufacturing, vol. 48, p. 926-931, ISSN 2351-9789, 2020.
[4]. Nottensteiner K., Sachtler A., Albu-Schäffer A., Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution, J Intell Robot Syst 101, 49, 2021.
[5]. Levine S., Pastor P., Krizhevsky A., Ibarz J., Quillen D., Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection, Int J Robotics Res., vol. 37, p. 421-436, 2018.
[6]. Tobin J., Fong R., Ray A., Schneider J., Zaremba W., Abbeel P., Domain randomization for transferring deep neural networks from simulation to the real world, Intelligent Robots and Systems (IROS), 2017.
[7]. Hennersperger C., Fuerst B., Virga S., Zettinig O., Frisch B., Neff T., Navab N., Towards MRI-based autonomous robotic US acquisitions: a first feasibility study, IEEE Transactions on Medical Imaging, 36, (2), p. 538-548. 2017.
[8]. Ganin Y., Ustinova E., Ajakan H., Germain P., Larochelle H., Laviolette F., Marchand M., Lempitsky V., Domain-adversarial training of neural networks, JMLR, 2016.
[9]. Ren S., He K., Girshick R., Sun J., Faster R-CNN: Towards realtime object detection with region proposal networks, Advances in Neural Information Processing Systems, 2015.
[10]. Ping L., Ji L., Zeng Y., Chen B., Zhang X., Real-time monitoring for manual operations with machine vision in smart manufacturing, Journal of Manufacturing Systems, vol.65, p. 709-719, ISSN 0278-6125, 2022.
[11]. Howard A., Andrew G., Menglong Z., Bo C., Kalenichenko D., Weijun W., Weyand T., Andreetto M., Hartwig A., MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv abs/1704.04861, 2017.
[12]. Teng X., Yu Q., Luo J., Wang G., Zhang X., Aircraft Pose Estimation Based on Geometry Structure Features and Line Correspondences, Sensors, 19, 2165, 2019.
[13]. Yang H., Jiang P., Wang F., Multi-View-Based Pose Estimation and Its Applications on Intelligent Manufacturing, Sensors (Basel), 20, (18), 5072, 2020.
Published
2022-12-15
How to Cite
1.
MARIN F-B, GURĂU G, MARIN M. Real-Time Assembly Operation Recognition. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Dec.2022 [cited 26Dec.2024];45(4):92-5. Available from: https://gup.ugal.ro/ugaljournals/index.php/mms/article/view/5825
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