Advanced thermal camera based system for object detection on rail tracks

Pavlovic, G.; Ćirić, Ivan; Ristić-Durrant, Danijela; Nikolić, Dušan; Simonović, Miloš; Ciric, V Milica; Banić, Milan

doi:10.2298/tsci18s5551p

Cited by 12 publications

(15 citation statements)

References 10 publications

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“…Several approaches employ thermal cameras as an easy means of detecting a variety of objects in the vicinity of the railway tracks [34,35]. These are especially beneficial in lowlight situations or for detecting humans or animals due to their body heat.…”

Section: Railway Obstacle Detectionmentioning

confidence: 99%

High-Precision Low-Cost Gimballing Platform for Long-Range Railway Obstacle Detection

Assaf

Cornelius

Cadena

et al. 2022

Sensors

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Increasing demand for rail transportation results transportation by rail, resulting in denser and more high-speed usage of the existing railway network, making makes new and more advanced vehicle safety systems necessary. Furthermore, high traveling speeds and the greatlarge weights of trains lead to long braking distances—all of which necessitates Long braking distances, due to high travelling speeds and the massive weight of trains, necessitate a Long-Range Obstacle Detection (LROD) system, capable of detecting humans and other objects more than 1000 m in advance. According to current research, only a few sensor modalities are capable of reaching this far and recording sufficiently accurate enoughdata to distinguish individual objects. The limitation of these sensors, such as a 1D-Light Detection and Ranging (LiDAR), is however a very narrow Field of View (FoV), making it necessary to use ahigh-precision means of orienting to target them at possible areas of interest. To close this research gap, this paper presents a novel approach to detecting railway obstacles by developinga high-precision pointing mechanism, for the use in a future novel railway obstacle detection system In this work such a high-precision pointing mechanism is developed, capable of targeting aiming a 1D-LiDAR at humans or objects at the required distance. This approach addresses To address the challenges of a low target pricelimited budget, restricted access to high-precision machinery and equipment as well as unique requirements of our target application., a novel pointing mechanism has been designed and developed. By combining established elements from 3D printers and Computer Numerical Control (CNC) machines with a double-hinged lever system, simple and cheaplow-cost components are capable of precisely orienting an arbitrary sensor platform. The system’s actual pointing accuracy has been evaluated using a controlled, in-door, long-range experiment. The device was able to demonstrate a precision of 6.179 mdeg, which is at the limit of the measurable precision of the designed experiment.

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Section: Railway Obstacle Detectionmentioning

confidence: 99%

High-Precision Low-Cost Gimballing Platform for Long-Range Railway Obstacle Detection

Assaf

Cornelius

Cadena

et al. 2022

Sensors

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“…The problem of designing a background model disappears as the thermal inertia of the environment is very high. Pavlovic et al propose [ 5 ] a solution in this domain. Obstacleboundaries are obtained using Canny edge detector.…”

Section: Related Workmentioning

confidence: 99%

“…Sensing systems may incorporate monitoring cameras, depth cameras, thermal cameras, radar, ultrasonic and LIDAR (light detection and ranging) sensors [ 3 , 4 , 5 , 6 ]. The devices provide a wide spectrum of information about the traffic at the RLC.…”

Section: Introductionmentioning

confidence: 99%

Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning

Pamuła

2021

Sensors

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The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the site increases the capability for reacting to dangerous situations. One such source is video from monitoring cameras. This paper presents a method for processing video data, using deep learning, for the determination of the state of the area (region of interest—ROI) vital for a safe passage of the train. The proposed approach is validated using video surveillance material from a number of RLC sites in Poland. The films include 24/7 observations in all weather conditions and in all seasons of the year. Results show that the recall values reach 0.98 using significantly reduced processing resources. The solution can be used as an auxiliary source of signals for train control systems, together with other sensor data, and the fused dataset can meet railway safety standards.

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“…The work in literature 17 proposed the use of a thermal camera, which creates an image based on the difference in temperature. The thermal imaging relies on the phenomenon of black-body radiation produced by all objects with a temperature above absolute zero, therefore, the system is unaffected with the change in weather or varying light conditions.…”

Section: Obstacle Detection Sensorsmentioning

confidence: 99%

A review of the technological developments for interlocking at level crossing

Fayyaz

Alexoulis-Chrysovergis

Southgate

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part F:

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A Level Crossing remains as one of the highest risk assets within the railway system often depending on the unpredictable behaviour of road and footpath users. For this purpose, interlocking through automated safety systems remains a key area for investigation. Within Europe, 2015–2016, 469 accidents at crossings were recorded of which 288 lead to fatalities and 264 lead to injuries. The European Union’s Agency for Railways has reported that Level Crossing fatalities account for just under 28% of all railway fatalities. This paper identifies suitable obstacle detection technologies and their associated algorithms that can be used to support risk reduction and management of Level Crossings. Furthermore, assessment and decision methods are presented to support their application. Finally state of the art and synergistic opportunity of which a combination of obstacle detection sensors with intelligent decisions layers such as Deep Learning are discussed which can provide robust interlocking decisions for rail applications. The sensor fusion of video camera and RADAR is a promising solution for Level Crossings. By applying additional sensing techniques such as RADAR imaging, further capabilities are added to the system, which can lead to a more robust approach.

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Advanced thermal camera based system for object detection on rail tracks

Cited by 12 publications

References 10 publications

High-Precision Low-Cost Gimballing Platform for Long-Range Railway Obstacle Detection

High-Precision Low-Cost Gimballing Platform for Long-Range Railway Obstacle Detection

Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning

A review of the technological developments for interlocking at level crossing

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