TY  - JOUR
PY  - 2021//
TI  - Enhancing railway maintenance safety using open-source computer vision
JO  - Journal of advanced transportation
A1  - Shin, Donghee
A1  - Jin, Jangwon
A1  - Kim, Jooyoung
SP  - e5575557
EP  - e5575557
VL  - 2021
IS  - 
N2  - As high-speed railways continue to be constructed, more maintenance work is needed to ensure smooth operation. However, this leads to frequent accidents involving maintenance workers at the tracks. Although the number of such accidents is decreasing, there is an increase in the number of casualties. When a maintenance worker is hit by a train, it invariably results in a fatality; this is a serious social issue. To address this problem, this study utilized the tunnel monitoring system installed on trains to prevent railway accidents. This was achieved by using a system that uses image data from the tunnel monitoring system to recognize railway signs and railway tracks and detect maintenance workers on the tracks. Images of railway signs, tracks, and maintenance workers on the tracks were recorded through image data. The Computer Vision OpenCV library was utilized to extract the image data. A recognition and detection algorithm for railway signs, tracks, and maintenance workers was constructed to improve the accuracy of the developed prevention system.<p />  <p>Language: en</p>
LA  - en
SN  - 0197-6729
UR  - http://dx.doi.org/10.1155/2021/5575557
ID  - ref1
ER  -