카테터 제조를 위한 토출영역 분할 기반센터링 방향 분류 시스템
Centering Direction Classification System for Discharge AreaSegmentation based on Catheter Manufacturing
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
This paper proposes a vision-based centering direction inference system designed to enhance the accuracy of equipment alignment during the initial setup phase of catheter manufacturing. This system addresses the challenges of centering tasks that are prone to human error and variability, aiming to significantly improve the reliability and efficiency of the production process. Through the construction of a comprehensive catheter centering dataset, this study employs the advanced capabilities of the SAM2 (Segment Anything Model 2) to extract precise mask data from catheter material extrusion sequences. These masks provide detailed insights into the positional changes from the initial to the final frame, which are essential for inferring the accurate centering direction. The algorithm developed categorizes the centering direction into three distinct classes: ‘Normal’, ‘Right’, and ‘Left’. It demonstrates robust performance with an accuracy of approximately 88.8% showcasing the system’s effectiveness across different material types. This level of accuracy is crucial for ensuring the quality and consistency of catheter products. Moreover, the paper discusses the potential for future research to expand the application of the SAM2 algorithm to enhance centering precision for a broader range of materials and catheter shapes. The ongoing development of this technology is expected to further automate and refine manufacturing processes, pushing the boundaries of what is currently achievable in high-precision manufacturing environments. The implementation of such advanced manufacturing technologies not only streamlines production but also facilitates a shift towards smarter manufacturing practices.
Abstract
This paper proposes a vision-based centering direction inference system designed to enhance the accuracy of equipment alignment during the initial setup phase of catheter manufacturing. This system addresses the challenges of centering tasks that are prone to human error and variability, aiming to significantly improve the reliability and efficiency of the production process. Through the construction of a comprehensive catheter centering dataset, this study employs the advanced capabilities of the SAM2 (Segment Anything Model 2) to extract precise mask data from catheter material extrusion sequences. These masks provide detailed insights into the positional changes from the initial to the final frame, which are essential for inferring the accurate centering direction. The algorithm developed categorizes the centering direction into three distinct classes: ‘Normal’, ‘Right’, and ‘Left’. It demonstrates robust performance with an accuracy of approximately 88.8% showcasing the system’s effectiveness across different material types. This level of accuracy is crucial for ensuring the quality and consistency of catheter products. Moreover, the paper discusses the potential for future research to expand the application of the SAM2 algorithm to enhance centering precision for a broader range of materials and catheter shapes. The ongoing development of this technology is expected to further automate and refine manufacturing processes, pushing the boundaries of what is currently achievable in high-precision manufacturing environments. The implementation of such advanced manufacturing technologies not only streamlines production but also facilitates a shift towards smarter manufacturing practices.
참고문헌 (20)
- M. Jolaei, "Design, Development and Force Control of a Tendon-driven Steerable Catheter with a Learning-based Approach", Doctor, pp.2020
- M. Bożek, "Improvement of Catheter Quality Inspection Process", MATEC Web of Conferences, EDP Sciences, 121(05002) (2017)
- W. A. Hyman, "Manufacturing Defects", Biomedical Safety & Standards, 49(11), pp.81~83 (2019)
- O. O. Vergara-Villegas, "Lean Manufacturing in the Developing World: Methodology, Case Studies and Trends from Latin America", pp.135~156 (2014)
- S. H. Lee, "Predicting the Heading Angle of Resin During Extrusion Using Semantic Segmentation Based on Edge-region Focal Loss", IEEE Transactions on Instrumentation and Measurement (2024)
- S. H. Jeong, "Development of Microcatheter Tube Extrusion Angle Estimation System Using Convolutional Neural Network Segmentation", Scientific Reports, 13(1), pp.18468~ (2023)
- F. A. Lievano-Martínez, "Intelligent Process Automation : An Application in Manufacturing Industry", Sustainability, 14(14), pp.8804~ (2022)
- R. Cioffi, "Artificial Intelligence and Machine Learning Applications in Smart Production : Progress, Trends, and Directions", Sustainability, 12(2), pp.492~ (2020)
- J. Xu, "A Review on AI for Smart Manufacturing : Deep Learning Challenges and Solutions", Applied Sciences, 12(16), pp.8239~ (2022)
- A. Kirillov, "Segment Anything", Proceedings of the IEEE, 2023, 4015~4026
- H. Li, "PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts", International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.389~399 (2024), Springer Nature Switzerland
- K. He, "Masked Autoencoders are Scalable Vision Learners", Proceedings of the IEEE, 2022, 16000~16009
- C. Ryali, "Hiera: A Hierarchical Vision Transformer Without the Bells-and-whistles", International Conference on Machine Learning, pp.29441~29454 (2023), PMLR
- H. Zhang, "Self-attention Generative Adversarial Networks", International Conference on Machine Learning, pp.7354~7363 (2019), PMLR
- Z. Huang, "Ccnet : Criss-cross Attention for Semantic Segmentation", Proceedings of the IEEE, 2019, 603~612
- M. Bramson, "Instability of FIFO Queueing Networks", The Annals of Applied Probability, 4(2), pp.414~431 (1994)
- Y. Xie, "A New Efficient Ellipse Detection Method", 2002 International Conference on Pattern Recognition, 2, pp.957~960 (2002), IEEE
- A. Fitzgibbon, "Direct Least Square Fitting of Ellipses", IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5), pp.476~480 (1999)