Detection and Recognition of Selected Class Railway Signs

Mikrut, S.; Mikrut, Zbigniew; Moskal, Agnieszka; Pastucha, Elżbieta

doi:10.1515/ipc-2015-0013

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Thanks to the improvements made to devices for geo-referencing, the absolute accuracy of the system is already high and amounts to about 1–2 cm. Scanning systems are used for 3D mapping of cities, creating 3D models of architectural objects, positioning and testing of deformations of road infrastructure, automatic recognition of road signs [ 1 ], measurements of rail infrastructure, including the measurement of the clearance gauge, etc .…”

Section: Review Of the Existing Mobile Systems Both The Ones Dedimentioning

confidence: 99%

Mobile Laser Scanning Systems for Measuring the Clearance Gauge of Railways: State of Play, Testing and Outlook

Mikrut

Kohut

Pyka

et al. 2016

Sensors

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The paper contains a survey of mobile scanning systems for measuring the railway clearance gauge. The research was completed as part of the project carried out for the PKP (PKP Polish Railway Lines S.A., Warsaw, Poland) in 2011–2013. The authors conducted experiments, including a search for the latest solutions relating to mobile measurement systems that meet the basic requirement. At the very least, these solutions needed to be accurate and have the ability for quick retrieval of data. In the paper, specifications and the characteristics of the component devices of the scanning systems are described. Based on experiments, the authors did some examination of the selected mobile systems to be applied for measuring the clearance gauge. The Riegl (VMX-250) and Z+F (Zoller + Fröhlich) Solution were tested. Additional test measurements were carried out within a 30-kilometer section of the Warsaw-Kraków route. These measurements were designed so as to provide various elements of the railway infrastructure, the track geometry and the installed geodetic control network. This ultimately made it possible to reduce the time for the preparation of geodetic reference measurements for the testing of the accuracy of the selected systems. Reference measurements included the use of the polar method to select profiles perpendicular to the axis of the track. In addition, the coordinates selected were well defined as measuring points of the objects of the infrastructure of the clearance gauge. All of the tested systems meet the accuracy requirements initially established (within the range of 2 cm as required by the PKP). The tested systems have shown their advantages and disadvantages.

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Section: Review Of the Existing Mobile Systems Both The Ones Dedimentioning

confidence: 99%

Mobile Laser Scanning Systems for Measuring the Clearance Gauge of Railways: State of Play, Testing and Outlook

Mikrut

Kohut

Pyka

et al. 2016

Sensors

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“…In this work models based on Gaussian distribution, 1D histograms and 3D histograms were evaluated. More advanced approaches like Gaussian Mixture Models [14], artificial neural networks [15] or typical machine learning [16] approach were not considered. However, if in future versions of the system, more, especially quite similar, colours should be recognized, than this methods could provide better performance and reliability.…”

Section: B the Analysed Colour Modelsmentioning

confidence: 99%

Shape and colour recognition of dishes for the purpose of customer service process automation in a self-service canteen

Kryjak

Krol

2015

Annals of Computer Science and Information Systems

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Abstract-In the article a vision system for shape and colour recognition of dishes (plates, bowls, mugs), which can be used to automate the process of customer service in a self-service canteen is described. In consists of three basic components: object segmentation using so-called background model subtraction, shape recognition using geometric invariant moments and SVM classifier, as well as colour recognition using a Gaussian model. In addition, recognition in case of close or abut objects using a distance transform like approach is presented. The solution was evaluated on a dedicated test stand with controlled LED lightning. A 98% accuracy was obtained on over 100 test images, which indicates that the solution could be used in business practise.

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Real-Time Detection and Recognition of Railway Traffic Signals Using Deep Learning

Staino

Suwalka

Mitra

et al. 2022

J. Big Data Anal. Transp.

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Automated detection and recognition of traffic signals are of great significance in railway systems. Autonomous driving solutions are well established for urban rail transportation systems. Many metro lines in service worldwide have reached the highest grade of automation where the train is automatically operated without any staff on board. However, autonomous driving is still an open challenge for mainline trains, due to the complexity of the mainline environment. In this context, automated recognition of wayside signals can help to minimise the risk of human error owing to low visibility and fatigue. It represents a key step towards the fully autonomous train. In this article we present a deep learning based approach for the above task. The You Only Look Once (YOLOv5) is used for detection and recognition of wayside signals. A heuristic is used to recognise blinking states. We consider FRSign dataset, a large collection of over 100,000 images of traffic signals from some of the trains in French Railways. A distilled and cleaned version of the dataset curated by us is used for training. The trained network has low computational overhead and can recognise traffic signals in real time and under diverse field conditions. It has robust performance even for complex scenes having multiple signals and light sources, and in adverse circumstances such as rain and night environments. The refined version of the dataset is published as open for validation and further research and development.

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Detection and Recognition of Selected Class Railway Signs

Cited by 3 publications

References 12 publications

Mobile Laser Scanning Systems for Measuring the Clearance Gauge of Railways: State of Play, Testing and Outlook

Mobile Laser Scanning Systems for Measuring the Clearance Gauge of Railways: State of Play, Testing and Outlook

Shape and colour recognition of dishes for the purpose of customer service process automation in a self-service canteen

Real-Time Detection and Recognition of Railway Traffic Signals Using Deep Learning

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