Railway Track Sleeper Detection in Low Altitude UAV Imagery Using Deep Convolutional Neural Network

Singh, Arun Kumar; Dwivedi, Arun Kant; Nahar, Nimish; Singh, Dharmendra

doi:10.1109/igarss47720.2021.9554542

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…Mathe et al (2016) discussed using a lightweight drone to detect problems such as missing indicators or cabling (Mathe, et al, 2016). Singh et al (2021) described how they recognized concrete sleepers on train tracks by taking low-altitude aerial photos using a DJI Phantom 3 drone. Their sleeper identification model was based on the YOLO v4 algorithm, which provided optimum speed and accuracy in the object recognition models compared to existing techniques (Singh, Swarup, Agarwal, & Singh, 2019).…”

Section: Rail Defect Identification: Wu Et Al (2018) Proposed An Imag...mentioning

confidence: 99%

Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Askarzadeh

Bridgelall

Tolliver

2023

J. Transp. Eng., Part A: Systems

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Drones have recently become a new tool in railway inspection and monitoring (RIM) worldwide, but there is still a lack of information about the specific benefits and costs. This study conducts a systematic literature review (SLR) of the applications, opportunities, and challenges of using drones for RIM. The SLR technique yielded 47 articles filtered from 7,900 publications from 2014 to 2022. The SLR found that key motivations for using drones in RIM are to reduce costs, improve safety, save time, improve mobility, increase flexibility, and enhance reliability. Nearly all the applications fit into the categories of defect identification, situation assessment, rail network mapping, infrastructure asset monitoring, track condition monitoring, and obstruction detection. The authors assessed the open technical, safety, and regulatory challenges. The authors also contributed a cost analysis framework, identified factors that affect drone performance in RIM, and offered implications for new theories, management, and impacts to society.

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Section: Rail Defect Identification: Wu Et Al (2018) Proposed An Imag...mentioning

confidence: 99%

Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Askarzadeh

Bridgelall

Tolliver

2023

J. Transp. Eng., Part A: Systems

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“…Singh et al used an object detection model based on the YOLOv4 algorithm to detect railway sleepers using UAV images. They showed that their proposed method could detect railway sleepers with 92% accuracy, 99.10% recall, and 99.08% average accuracy (mAP) success rates [18].…”

Section: Figure 2 Block Diagram For Proposed Approach [14]mentioning

confidence: 99%

A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50

Santur¹,

Yilmazer²,

Karaköse³

et al. 2022

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Rail transportation systems, which are used as one of the most common means of transportation worldwide, should be regularly inspected to prevent accidents that may occur. The rail condition monitoring can be performed in high accuracy and real time using computer vision, deep learning algorithms today. In this study, a new deep learning based approach using 3D laser cameras for rail inspection is presented. In the proposed approach, two 3D laser cameras placed on a real train, seeing the rail line from the left and right surfaces were used. These data consisting of sensitive distance value constitute the input data of the ResNet50 transfer learning model. The training was carried out on Nvidia Cuda supported graphics processing units using ResNet50 Convolutional Neural Network. During the test phase, the operation speed and accuracy rate of the method was measured by repeating the process on real-time rail profiles. The accuracy rate was calculated as 94%. As a result a new approach is presented based on deep learning using 3D laser cameras for rail inspection is presented.

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“…Ray ile bağlantı elemanı arasında herhangi bir yabancı cisim olmadığını varsayarak bu bölgeleri çıkarmak için Mask R-CNN algoritmasını kullanmışlardır. Singh ve arkadaşları [13] İHA görüntülerini kullanarak demiryolu traverslerini tespit etmeyi amaçlamışlardır.…”

Section: Li̇teratür çAlişmalariunclassified

Multiple Fault Detection in Railway Components with Mask R-CNN Deep Neural Network

Yilmazer

Karaköse

Aydın

et al. 2022

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Demiryolu birçok yolcunun aynı anda seyahat edebilmesine olanak tanıyan aynı zamanda yük taşımacılığında da sıklıkla kullanılan bir ulaşım çeşididir. Ulaşımda kazalar ve aksamalar meydana gelmemesi için hattın periyodik olarak kontrolünün sağlanması ve hatalı bileşenlerin belirlenerek onarılması gerekmektedir. Raylı ulaşım sistemlerinin güvenliğinin sağlanması için yapılan manuel denetimlere alternatif olarak, son zamanlarda görüntü işleme algoritmaları ve derin öğrenme algoritmaları kullanılarak temassız, hızlı ve güvenilir sonuçlar veren hata tespit yöntemleri geliştirilmiştir. Bu çalışmada sağlıklı olan traversler ve travers üzerinde meydana gelen çeşitli hataların tespit edilmesine yönelik Mask R-CNN derin sinir ağı mimarisi kullanılarak yeni bir yöntem önerildi. Üç farklı hata türü ve sağlıklı travers olmak üzere toplamda dört farklı sınıf etiketi ile etiketlenen gerçek demiryolu görüntüleri kullanılarak model eğitimi ve eğitilen modelin test edilmesi sağlandı. Değerlendirme metrikleri hesaplanarak modelin başarı performansı ölçüldü. Sağlıklı ve hatalı olan traversleri belirlemede modelin doğruluğu %95 olarak belirlendi.

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Railway Track Sleeper Detection in Low Altitude UAV Imagery Using Deep Convolutional Neural Network

Cited by 11 publications

References 8 publications

Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Systematic Literature Review of Drone Utility in Railway Condition Monitoring

A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50

Multiple Fault Detection in Railway Components with Mask R-CNN Deep Neural Network

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