Notice of retraction
Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English    日本語

Instructions for manuscript preparation
English    日本語

Template
English

Publisher
 MYU K.K.
 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.


MYU Research

(proofreading and recording)


MYU K.K.
(translation service)


The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Copyright(C) MYU K.K.
S&M2349 Research Paper of Special Issue

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

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. Yolo v3 is also used instead of earlier convolution neural networks 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 1920 × 1080 resolution at a speed of up to 35 km/h. Six types of normal fasteners and four defective 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 Global Positioning System (GPS) positions recorded by the sport camera. The nearest hectometer stake and the offset of the defective fastener were calculated to assist track workers to find the defective fasteners and fix them.

Corresponding author: Chen-Chiung Hsieh




Forthcoming Regular Issues


Forthcoming Special Issues

Special issue on Novel Materials and Sensing Technologies on Electronic and Mechanical Devices (1)
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Hsien-Wei Tseng (Longyan University)
Call for paper


Special Issue on New Trends in Smart Sensor Systems
Guest editor, Takahiro Hayashi (Kansai University)
Call for paper


Special Issue on Intelligent Sensing Control Analysis, Optimization and Automation
Guest editor, Cheng-Chi Wang (National Chin-Yi University of Technology)


Special Issue on International Conference on BioSensors, BioElectronics, BioMedical Devices, BioMEMS/NEMS and Applications 2019 (Bio4Apps 2019)
Guest editor, Hirofumi Nogami and Masaya Miyazaki (Kyushu University)
Conference website
Call for paper


Special Issue on Materials, Devices, Circuits, and Analytical Methods for Various Sensors (4)
Guest editor, Chien-Jung Huang (National University of Kaohsiung), Cheng-Hsing Hsu (National United University), Ja-Hao Chen (Feng Chia University), and Wei-Ling Hsu (Huaiyin Normal University)
Conference website


Special Issue on Geomatics Technologies for the Realization of Smart Cities (1) and (2)
Guest editor, He Huang and XiangLei Liu (Beijing University of Civil Engineering and Architecture)
Call for paper


Copyright(C) MYU K.K. All Rights Reserved.