EfficientNet Architecture Family Analysis on Railway Track Defects

Author:

Rengel Jon,Santos Matilde,Pandit Ravi

Publisher

Springer International Publishing

Reference15 articles.

1. International Energy Agency: The Future of Rail (2019)

2. Li, Q., Ren, S.: A real-time visual inspection system for discrete surface defects of rail heads. IEEE 61(8), 2189–2199 (2012)

3. Li, Q., Ren, S.: A visual detection system for rail surface defects. IEEE 42(6), 1531–1542 (2012)

4. James, A., et al.: TrackNet - a deep learning based fault detection for railway track inspection. IEEE 1, 1–5 (2018)

5. Yaman, O., Karaköse, M., Ak, E., Ayd, I.: Ray Yüzeyi için Görüntü İş leme Tabanl ı Ar ı za Tespit Yakla ş ı m ı Image Processing Based Fault Detection Approach for Rail Surface. In: 2015 23nd Signal Processing and Communications Applications Conference (SIU), p. 4. IEEE (2015)

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A study on the application of convolutional neural networks for the maintenance of railway tracks;Discover Artificial Intelligence;2024-05-02

2. Deep Convolutional Neural Networks for Rail Surface Defect Perception;2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML);2023-11-03

3. TrackSafe: A comparative study of data-driven techniques for automated railway track fault detection using image datasets;Engineering Applications of Artificial Intelligence;2023-10

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