S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials
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Sensors and Materials, Volume 32, Number 10(3) (2020)
Copyright(C) MYU K.K.
pp. 3429-3442
S&M2349 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2921
Published: October 30, 2020

Offline Deep-learning-based Defective Track Fastener Detection and Inspection System [PDF]

Chen-Chiung Hsieh, Ya-Wen Lin, Li-Hung Tsai, Wei-Hsin Huang, Shang-Lin Hsieh, and Wei-Hung Hung

(Received April 9, 2020; Accepted August 11, 2020)

Keywords: track fastener, defect inspection, deep learning, Yolo model, fastener positioning

Railway track fasteners are used to fasten the railway track onto the crosstie and improve the train’s stability and safety. Automatic detection systems have been developed for track safety. Most of these systems deployed line scan sensors to capture high-quality track images. These sensors can capture high-resolution images, but they are also expensive. In addition, the recognition kernels range from traditional computer vision to deep learning methods. In this study, we set up a track fastener sensing device on a flat track car by using general sport cameras and LED lamps to capture images of track fasteners. You Only Look Once (Yolo) v3 is also used instead of earlier convolution neural networks (CNNs) for defect inspection. A cloud server is built for users to queue their captured fastener videos to the first buffer for upload, and uploaded videos can be queued to a second buffer for defective track fastener detection. The trained Yolo v3 neural network classification module is encapsulated as a web application interface (API) for performing the task. In experiments, track fastener videos along a total of 70 km of track were captured with a resolution of 1920 × 1080 at a speed of up to 35 km/h. Six normal and four defective fastener types were defined for inspection. We split the dataset into 80% for training and 20% for testing. The average precision rates for normal and defective fasteners were 83 and 89%, respectively. Finally, the coordinates of defective fasteners were interpolated from GPS positions recorded by a sport camera. The nearest hectometer stake and the offset of each defective fastener were calculated to assist track workers to find the defective fasteners and fix them.

Corresponding author: Chen-Chiung Hsieh


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Cite this article
Chen-Chiung Hsieh, Ya-Wen Lin, Li-Hung Tsai, Wei-Hsin Huang, Shang-Lin Hsieh, and Wei-Hung Hung, Offline Deep-learning-based Defective Track Fastener Detection and Inspection System, Sens. Mater., Vol. 32, No. 10, 2020, p. 3429-3442.



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