A Real-Time Track Fasteners Classification System Based on Yolov4-Tiny Model

Trains are one of the main transportations for people, and track maintenance has become an important topic today. In the past, maintainer walked through the track to see if the track is damaged or not, which is time-consuming and labor-intensive. This study utilizes Yolov4-tiny neural network to aut...

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Bibliographic Details
Published in2022 IEEE International Conference on Consumer Electronics - Taiwan pp. 41 - 42
Main Authors Hsu, Ti-Yun, Hsieh, Chen-Chiung
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.07.2022
Subjects
Costs
Fasteners
Inspection
Maintenance engineering
Manuals
Neural networks
Transportation
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Summary:Trains are one of the main transportations for people, and track maintenance has become an important topic today. In the past, maintainer walked through the track to see if the track is damaged or not, which is time-consuming and labor-intensive. This study utilizes Yolov4-tiny neural network to automatically identify 10 classes of track missing from the forward and 4 classes of track missing, 2 classes of normal track from the sideways in real time. Each of the precision is 92% and 96%, and the recall rate is 91% and 94%, which can effectively improve the maintenance efficiency and reduce the manpower demand.
ISSN:2575-8284
DOI:10.1109/ICCE-Taiwan55306.2022.9869046