YOLO-VSI: An Improved YOLOv8 Model for Detecting Railway Turnouts Defects in Complex Environments
Source abstract
Abstract
Railway turnouts often develop defects such as chipping, cracks, and wear during use. If not detected and addressed promptly, these defects can pose significant risks to train operation safety and passenger security. Despite ... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Non-Destructive Testing Techniques · Industrial Vision Systems and Defect Detection · Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
Show source-provided references
1. Risk management in transportation safety system
2. The method of assessing traffic safety in railway transport
3. A method of classifying railway sleepers and surface defects in real environment10.1109/JSEN.2020.3026173 ↗
4. Research on defect detection method for high-reflective-metal surface based on high dynamic range imaging10.1016/j.ijleo.2020.164349 ↗
5. R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” presented at the IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Columbus, OH, USA, Jun. 23–28, 2014, pp. 580–587.10.1109/CVPR.2014.81 ↗
6. R. Girshick, “Fast R-CNN,” presented at the IEEE Int. Conf. Comput. Vis. (ICCV), Santiago, Chile, Dec. 11–18, 2015, pp. 1440–1448.
7. DF-SSD: An improved SSD object detection algorithm based on DenseNet and feature fusion10.1109/ACCESS.2020.2971026 ↗
8. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” presented at the IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, USA, Jun. 26–Jul. 1, 2016, pp. 779–788.10.1109/CVPR.2016.91 ↗
9. C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” presented at the IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Vancouver, BC, Canada, Jun. 18–22, 2023, pp. 7464–7475.10.1109/CVPR52729.2023.00721 ↗
10. C. Y. Wang, I. H. Yeh, and H. Y. M. Liao, “YOLOv9: Learning what you want to learn using programmable gradient information,” 2024, arXiv:2402.13616.10.1007/978-3-031-72751-1_1 ↗
11. N. Carion, F. Massa, G. Synnaeve, N. Usuniere, A. Kirillov and S. Zagoruyko, “End-to-end object detection with transformers,” presented at the Eur. Conf. Comput. Vis. (ECCV), Glasgow, UK, Aug. 23–28, 2020, pp. 213–229.10.1007/978-3-030-58452-8_13 ↗
12. Y. Zhao et al., “DETRs beat YOLOs on real-time object detection,” presented at the IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Seattle, WA, USA, Jun. 16–21, 2024, pp. 16965–16974.10.1109/CVPR52733.2024.01605 ↗
13. Surface defect detection of steel strips based on improved YOLOv410.1016/j.compeleceng.2022.108208 ↗
14. Rail surface damage detection method based on improved U-Net convolutional neural network
15. Attention-guided multi-granularity fusion model for video summarization10.1016/j.eswa.2024.123568 ↗
16. Steel surface defect detection method based on improved YOLOX10.1109/ACCESS.2024.3374869 ↗
17. Surface defect detection algorithm based on feature-enhanced YOLO
18. Efficient detection model of steel strip surface defects based on YOLO-V710.1109/ACCESS.2022.3230894 ↗
19. A light weight multi-scale feature fusion steel surface defect detection model based on YOLOv810.1088/1361-6501/ad296d ↗
20. Rail surface defect detection using a transformer-based network
21. SFSANet: Multiscale object detection in remote sensing image based on semantic fusion and scale adaptability10.1109/TGRS.2024.3510781 ↗
22. A study on railway surface defects detection based on machine vision10.3390/e23111437 ↗
23. An improved YOLOv8 algorithm for rail surface defect detection10.1109/ACCESS.2024.3380009 ↗
24. Ultralytics,“YOLOv8,” 2023, Accessed: Jan. 12, 2024. [Online]. Available: https://github.com/ultralytics/ultralytics
25. H. Li, P. Xiong, J. An, and L. Wang, “Pyramid attention network for semantic segmentation,” 2018, arXiv: 1805.10180.
26. Limited agricultural spectral dataset expansion based on generative adversarial networks10.1016/j.compag.2023.108385 ↗
27. Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway10.1088/1361-6501/ad3a05 ↗
28. Transformer in transformer
29. A new approach to linear filtering and prediction problems10.1115/1.3662552 ↗
30. A. Gu and T. Dao, “Mamba: Linear-time sequence modeling with selective state spaces,” 2023, arXiv:2312.00752.
31. L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu and X. Wang, “Vision Mamba: Efficient visual representation learning with bidirectional state space model,” 2024, arXiv:2401.09417.
32. YOLOv8-MPEB small target detection algorithm based on UAV images
33. R. Sunkara and T. Luo, “No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects,” presented at the Joint Eur. Conf. Mach. Learn. Knowl. Discov. Databases, Grenoble, France, Sep. 19–23, 2022, pp. 443–459. doi: 10.1007/978-3-031-26409-2_27.10.1007/978-3-031-26409-2_27 ↗
34. Y. Cui, W. Ren, and A. Knoll, “Omni-kernel network for image restoration,” presented at the AAAI Conf. Artif. Intell., Vancouver, BC, Canada, Feb, 2024, vol. 38, no. 2, pp. 1426–1434.10.1609/aaai.v38i2.27907 ↗
35. H. Zhang, C. Xu, and S. Zhang, “Inner-IoU: More effective intersection over union loss with auxiliary bounding box,” 2023, arXiv:2311.02877.