Vision based rail track extraction and monitoring through drone imagery

Singh, Arun Kumar; Swarup, Anushka; Agarwal, Ankush; Singh, Dharmendra

doi:10.1016/j.icte.2017.11.010

Cited by 74 publications

(38 citation statements)

References 10 publications

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“…These data tend to be multi-modal, unstructured, decentralized, heterogeneous, fast-flowing and highly nonlinear [16], posing significant challenges to traditional data-driven methods in PHM applications. For example, in two studies, Unmanned Aerial Vehicles (UAV) were used to carry out regular inspection of railway tracks [17], [18]. Images and videos taken by the drones can be analyzed to detect potential track defects, such as squats, poor-quality insulated joints, structural damage and so on.…”

Section: Introductionmentioning

confidence: 99%

“…Images and videos taken by the drones can be analyzed to detect potential track defects, such as squats, poor-quality insulated joints, structural damage and so on. However, traditional methods of analysis rely on domain expertise to extract useful features like edges, lines and textures, which can then be fed to other learning algorithms [17], [18]. These hand-crafted features may be subjective, implying low efficiency and high labor cost.…”

Section: Introductionmentioning

confidence: 99%

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A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

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Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

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“…Among these research works, one can mention the application of drones in monitoring railway tracks by detecting the vanishing point in the images [ 22 ]. Singh et al [ 23 ] applied edge detection algorithms in UAV images to identify rail lines and calculated the geometric parameter of the rail gauge. Additionally, in 2019, Banic et al, by processing the video frames taken by a UAV using the edge detection operator and the K-nearest neighbor (KNN) classifier, identified rail tracks for automatic inspection [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud

Sahebdivani

Arefi

Maboudi

2020

Sensors

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The expansion of the railway industry has increased the demand for the three-dimensional modeling of railway tracks. Due to the increasing development of UAV technology and its application advantages, in this research, the detection and 3D modeling of rail tracks are investigated using dense point clouds obtained from UAV images. Accordingly, a projection-based approach based on the overall direction of the rail track is proposed in order to generate a 3D model of the railway. In order to extract the railway lines, the height jump of points is evaluated in the neighborhood to select the candidate points of rail tracks. Then, using the RANSAC algorithm, line fitting on these candidate points is performed, and the final points related to the rail are identified. In the next step, the pre-specified rail piece model is fitted to the rail points through a projection-based process, and the orientation parameters of the model are determined. These parameters are later improved by fitting the Fourier curve, and finally a continuous 3D model for all of the rail tracks is created. The geometric distance of the final model from rail points is calculated in order to evaluate the modeling accuracy. Moreover, the performance of the proposed method is compared with another approach. A median distance of about 3 cm between the produced model and corresponding point cloud proves the high quality of the proposed 3D modeling algorithm in this study.

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“…Sigha [9] investigated the possibilities of computer vision-based monitoring with UAV imagery. The UAV camera provides high quality images that contain large information for monitoring and analysis.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Machine Vision Based Railway Infrastructure Inspection and Monitoring Using Uav

et al. 2019

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Traditionally, railway inspection and monitoring are considered a crucial aspect of the system and are done by human inspectors. Rapid progress of the machine vision-based systems enables automated and autonomous rail track detection and railway infrastructure monitoring and inspection with flexibility and ease of use. In recent years, several prototypes of vision based inspection system have been proposed, where most have various vision sensors mounted on locomotives or wagons. This paper explores the usage of the UAVs (drones) in railways and computer vision based monitoring of railway infrastructure. Employing drones for such monitoring systems enables more robust and reliable visual inspection while providing a cost effective and accurate means for monitoring of the tracks. By means of a camera placed on a drone the images of the rail tracks and the railway infrastructure are taken. On these images, the edge and feature extraction methods are applied to determine the rails. The preliminary obtained results are promising.

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Vision based rail track extraction and monitoring through drone imagery

Cited by 74 publications

References 10 publications

A Review on Deep Learning Applications in Prognostics and Health Management

A Review on Deep Learning Applications in Prognostics and Health Management

Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud

Intelligent Machine Vision Based Railway Infrastructure Inspection and Monitoring Using Uav

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