Scopus
🔓 Açık Erişim
An adaptive fault diagnosis approach using pipeline implementation for railway inspection
Turkish Journal of Electrical Engineering and Computer Sciences · Ocak 2018
Makale Bilgileri
DergiTurkish Journal of Electrical Engineering and Computer Sciences
Yayın TarihiOcak 2018
Cilt / Sayfa26 · 987-998
Scopus ID2-s2.0-85044993684
Erişim🔓 Açık Erişim
Özet
Railway tracks must be periodically inspected. This study proposes a new approach for eliminating two major disadvantages experienced during rail inspection applications performed via computer vision. The first is the blurring effect on images, resulting from physical vibration during movement on the rail lines. This effect significantly reduces the high accuracy rate expected from anomaly inspection algorithms. The second disadvantage is the need to operate in real time. This study presents a new three-stage computer vision method approach that eliminates both disadvantages. First, a three-stage pipeline architecture is implemented and IMU-assisted blur detection is performed on images taken from the left and right rail lines. Next, a convolutional neural network is used for learning. In the third test stage, anomaly detection and classification training are conducted. By performing the implementation with parallel programming on graphic processing units, a highly accurate, cost-effective computer vision rail inspection, based on image processing and capable of operating in real time, is successfully carried out.
Yazarlar (3)
1
Yunus Santur
2
Mehmet Karaköse
3
Erhan Akin
ORCID: 0000-0001-6476-9255
Anahtar Kelimeler
Blur removal
Convolutional neural network
Pipeline
Railway inspection
Kurumlar
Firat Üniversitesi
Elazig Turkey
Metrikler
15
Atıf
3
Yazar
4
Anahtar Kelime