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Cited by 17Advanced Neural Network ApplicationsIndustrial Vision Systems and Defect DetectionMedical Image Segmentation Techniques

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

An improved YOLOv8 for fiber bundle segmentation in X-ray computed tomography images of 2.5D composites to build the finite element model

Zhang, Sheng, Wang, Kaiyu, Zhang, Huajun, Wang, Tong, Gao, Xiguang, Song, Yingdong, Wang, Fang·2024·Composites Part A: Applied Science and Manufacturing
DOI 10.1016/j.compositesa.2024.108337
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Crossref · 10.1016/j.compositesa.2024.108337

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Citations

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Research works printed but not linked: 23

  1. Sharma, 2012, Fiber bundle push-out test and image-based finite element simulation for 3D carbon/carbon composites, Carbon N Y, 50, 2717, 10.1016/j.carbon.2012.02.030 · DOI
  2. Emerson, 2017, Individual fiber segmentation from 3D X-ray computed tomography for characterising the fibre orientation in unidirectional composite materials, Compos A Appl Sci Manuf, 97, 83, 10.1016/j.compositesa.2016.12.028 · DOI
  3. Naouar, 2014, Meso-scale FE analyses of textile composite reinforcement deformation based on X-ray computed tomography, Compos Struct, 116, 165, 10.1016/j.compstruct.2014.04.026 · DOI
  4. Wijaya, 2019, An automatic methodology to CT-scans of 2D woven textile fabrics to structured finite element and voxel meshes, Compos A Appl Sci Manuf, 125, 10.1016/j.compositesa.2019.105561 · DOI
  5. Huang, 2019, Reconstruction of mesostructural material twin models of engineering textiles based on Micro-CT Aided Geometric Modeling, Compos A Appl Sci Manuf, 124
  6. Benezech, 2019, Variational segmentation of textile composite preforms from X-ray computed tomography, Compos Struct, 230, 111496.1
  7. Naouar, 2015, 3D composite reinforcement meso FE analyses based on X-ray computed tomography, Compos Struct, 132, 1094, 10.1016/j.compstruct.2015.07.005 · DOI
  8. Sencu, 2016, Generation of micro-scale finite element models from synchrotron X-ray CT images for multidirectional carbon fibre reinforced composites, Compos A Appl Sci Manuf, 91A, 85, 10.1016/j.compositesa.2016.09.010 · DOI
  9. Liu, 2020, Refine reconstruction and verification of meso-scale modeling of three- dimensional five -directional braided composites from X-ray computed tomography data, Compos Struct, 245
  10. Gao, 2017, X-ray computed tomography based microstructure reconstruction and numerical estimation of thermal conductivity of 2.5D ceramic matrix composite, Ceram Int, 43, 9790, 10.1016/j.ceramint.2017.04.157 · DOI
  11. Lu, 2023, Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites, Robotics Computer Integrated Manuf.: Int. J. Manuf. Product Process Develop, 79, 10.1016/j.rcim.2022.102431 · DOI
  12. Xie, 2022, An intelligent defect detection system for warp-knitted fabric, Text Res J, 92, 1394
  13. Yu, 2023, Two-stage intelligent detection system of pixel-level classification and damage type recognition for membrane materials, Mater Lett, 347
  14. Li H, Li J, Wei H, Liu Z, Zhan Z, Ren Q. Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles. arXiv preprint arXiv:2206.02424, 2022. Doi: 10.48550/arXiv.2206.02424. · DOI
  15. Bolya, 2022, YOLACT plus plus better real-time instance segmentation, Ieee Trans Pattern Anal Mach Intell, 44, 1108, 10.1109/TPAMI.2020.3014297 · DOI · Paper page
  16. Chen H, Wang Y, Guo J, D Tao. VanillaNet: the power of minimalism in deep learning. arXiv preprint arXiv:2305.12972, 2023. Doi: 10.48550/arXiv.2305.12972. · DOI
  17. Zheng, 2021, Enhancing geometric factors in model learning and inference for object detection and instance segmentation, IEEE Trans Cybern, 52, 8574, 10.1109/TCYB.2021.3095305 · DOI · Paper page
  18. Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S. Generalized intersection over union: a metric and a loss for bounding box regression. Long Beach; 2019. p. 658-666. Doi: 10.1109/CVPR.2019.00075. · DOI · Paper page
  19. Yi, 2023, Research on underwater small target detection algorithm based on improved YOLOv7, IEEE Access, 11, 66818, 10.1109/ACCESS.2023.3290903 · DOI
  20. Zhang, 2022, Focal and efficient IOU loss for accurate bounding box regression, Neurocomputing, 506, 146, 10.1016/j.neucom.2022.07.042 · DOI
  21. Tong Z, Chen Y, Xu Z, Yu R. Wise-IoU: bounding box regression loss with dynamic focusing mechanism. arXiv preprint arXiv:2301.10051, 2023. Doi: 10.48550/arXiv.2301.10051. · DOI
  22. Jia, 2021, Adopting traditional image algorithms and deep learning to build the finite model of a 2.5D composite based on X-Ray computed tomography, Compos Struct, 275
  23. Garland, 1997, Surface simplification using quadric error metrics, ACM SIGGRAPH Computer Graphics, 209

Cited by

Metadata

Authors
Zhang, S., Wang, K., Zhang, H., Wang, T., Gao, X., Song, Y., & Wang, F.
Affiliations
  • Chinese People's Liberation Army (CN)
  • Nanjing Forestry University (CN)
  • Ministry of Industry and Information Technology (CN)
  • State Key Laboratory of Mechanical Structure Mechanics and Control (CN)
Corresponding author
Zhang, Sheng
ORCID
Venue
Composites Part A: Applied Science and Manufacturing
Publisher
Elsevier BV
Published
2024-10
Volume
185
Pages
108337
ISSN
1359-835X (ISSN-L)
DOI
10.1016/j.compositesa.2024.108337
OpenAlex
W4400110111
Cited by
17
References
2
Citations by year
2026: 9 · 2025: 6 · 2024: 2
FWCI
2.38
Type
journal article
Language
en
Open access
closed
Licence
Locations
Composites Part A Applied Science and Manufacturing (publishedVersion)
Funders
  • National Natural Science Foundation of China (52202098)
  • National Major Science and Technology Projects of China (Y2022-VI-0003-0034)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
YOLOv8, computed tomography, ceramic matrix composites, finite element model, lightweight object detection, Slim-Neck, Wise-IoU loss

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