15.3.3.11 Railroads, Inspection, Obstacles

Chapter Contents (Back)
Obstacle Detection. Collision Detection. Railroads. Trains. See also Obstacle Dectection, Other Vehicles, Objects on the Road. See also Rail Traffic Controls, Trains.

Mermec Group,
2010.
WWW Link. Vendor, Railroad Inspection.

Trosino, M.[Michael], Cunningham, J.J.[John J.], Shaw, III, A.E.[Alfred E.],
Automated track inspection vehicle and method,
US_Patent6,064,428, May 16, 2000
WWW Link. BibRef 0005
And: US_Patent6,356,299, Mar 12, 2002
WWW Link. BibRef

Mandriota, C., Nitti, M., Ancona, N., Stella, E., Distante, A.,
Filter-based feature selection for rail defect detection,
MVA(15), No. 4, October 2004, pp. 179-185.
Springer DOI 0410
BibRef

Mandriota, C., Stella, E., Nitti, M., Ancona, N., Distante, A.,
Rail Corrugation Detection by Gabor Filtering,
ICIP01(II: 626-628).
IEEE DOI 0108
BibRef

Labarile, A., Stella, E., Ancona, N., Distante, A.,
Ballast 3D reconstruction by a matching pursuit based stereo matcher,
IVS04(653-657).
IEEE DOI 0411
BibRef

Mazzeo, P.L., Nitti, M., Stella, E., Distante, A.,
Visual recognition of fastening bolts for railroad maintenance,
PRL(25), No. 6, 19 April 2004, pp. 669-677.
Elsevier DOI 0405
BibRef

Kim, Z.[ZuWhan], Cohn, T.E.,
Pseudoreal-time activity detection for railroad grade-crossing safety,
ITS(5), No. 4, December 2004, pp. 319-324.
IEEE Abstract. 0501
BibRef

Marino, F.[Francescomaria], Distante, A.[Arcangelo], Mazzeo, P.L.[Pier Luigi], Stella, E.[Ettore],
A Real-Time Visual Inspection System for Railway Maintenance: Automatic Hexagonal-Headed Bolts Detection,
SMC-C(37), No. 3, May 2007, pp. 418-428.
IEEE DOI 0704
BibRef

Tsang, C.W., Ho, T.K.,
Optimal Track Access Rights Allocation for Agent Negotiation in an Open Railway Market,
ITS(9), No. 1, March 2008, pp. 68-82.
IEEE DOI 0803
BibRef

Garcia, J.J., Ureña, J., Hernandez, A., Mazo, M., Jimenez, J.A., Alvarez, F.J., de Marziani, C., Jimenez, A., Diaz, M.J., Losada, C., Garcia, E.,
Efficient Multisensory Barrier for Obstacle Detection on Railways,
ITS(11), No. 3, September 2010, pp. 702-713.
IEEE DOI 1003
BibRef

Wilson, A.[Andrew],
Riding the Rails,
VisSys(16), No. 1, January 2011. Survey, Rail Inspection. Railway tunnels, bridges, and underpasses can be imaged at high speeds using linescan and area-array cameras. BibRef 1101

Hensel, S., Hasberg, C., Stiller, C.,
Probabilistic Rail Vehicle Localization With Eddy Current Sensors in Topological Maps,
ITS(12), No. 4, December 2011, pp. 1525-1536.
IEEE DOI 1112
BibRef

Huang, Y.P.[Ya-Ping], Luo, S.W.[Si-Wei], Wang, S.C.[Sheng-Chun],
Combining Boundary and Region Information with Bolt Prior for Rail Surface Detection,
IEICE(E95-D), No. 2, February 2012, pp. 690-693.
WWW Link. 1202
BibRef

Hernandez, A., Perez, M.C., Garcia, J.J., Jimenez, A., Garcia, J.C., Espinosa, F., Mazo, M., Urena, J.,
FPGA-Based Track Circuit for Railways Using Transmission Encoding,
ITS(13), No. 2, June 2012, pp. 437-448.
IEEE DOI 1206
BibRef

Li, Q.Y.[Qing-Yong], Huang, Y.P.[Ya-Ping], Liang, Z.P.[Zheng-Ping], Luo, S.W.[Si-Wei],
Thresholding Based on Maximum Weighted Object Correlation for Rail Defect Detection,
IEICE(E95-D), No. 7, July 2012, pp. 1819-1822.
WWW Link. 1208
BibRef

Xu, Z., Wang, W., Sun, Y.,
Performance Degradation Monitoring for Onboard Speed Sensors of Trains,
ITS(13), No. 3, September 2012, pp. 1287-1297.
IEEE DOI 1209
BibRef

Nassu, B.T., Ukai, M.,
A Vision-Based Approach for Rail Extraction and its Application in a Camera Pan-Tilt Control System,
ITS(13), No. 4, December 2012, pp. 1763-1771.
IEEE DOI 1212
BibRef

Zhang, Y.X.[Yi-Xin], Wang, S.[Shun], Zhang, X.[Xuping], Xie, F.[Fei], Wang, J.[Jiaqi],
Freight train gauge-exceeding detection based on three-dimensional stereo vision measurement,
MVA(24), No. 3, April 2013, pp. 461-475.
WWW Link. 1303
BibRef

Shafiullah, G.M., Azad, S.A., Ali, A.B.M.S.,
Energy-Efficient Wireless MAC Protocols for Railway Monitoring Applications,
ITS(14), No. 2, 2013, pp. 649-659.
IEEE DOI 1307
Rail transportation; Energy efficiency BibRef

Chen, X.X.[Xiang-Xian], Zhou, G.S.[Gong-Shuang], Yang, Y.[Yi], Huang, H.[Hai],
A Newly Developed Safety-Critical Computer System for China Metro,
ITS(14), No. 2, 2013, pp. 709-719.
IEEE DOI 1307
railways; really the computer system, not vision. BibRef

Resendiz, E., Hart, J.M., Ahuja, N.,
Automated Visual Inspection of Railroad Tracks,
ITS(14), No. 2, 2013, pp. 751-760.
IEEE DOI 1307
automated visual inspection; spectral estimation method BibRef

Dong, H., Ning, B., Chen, Y., Sun, X., Wen, D., Hu, Y., Ouyang, R.,
Emergency Management of Urban Rail Transportation Based on Parallel Systems,
ITS(14), No. 2, 2013, pp. 627-636.
IEEE DOI 1307
Rail transportation BibRef

Wang, H., Schmid, F., Chen, L., Roberts, C., Xu, T.,
A Topology-Based Model for Railway Train Control Systems,
ITS(14), No. 2, 2013, pp. 819-827.
IEEE DOI 1307
signaling; Safety BibRef

Wang, J., Wang, J., Roberts, C., Chen, L.,
Parallel Monitoring for the Next Generation of Train Control Systems,
ITS(16), No. 1, February 2015, pp. 330-338.
IEEE DOI 1502
Communication system signaling BibRef

Wang, H., Yu, F.R., Zhu, L., Tang, T., Ning, B.,
Finite-State Markov Modeling for Wireless Channels in Tunnel Communication-Based Train Control Systems,
ITS(15), No. 3, June 2014, pp. 1083-1090.
IEEE DOI 1407
Antenna measurements BibRef

Sohn, K.[Keemin],
Optimizing Train-Stop Positions Along a Platform to Distribute the Passenger Load More Evenly Across Individual Cars,
ITS(14), No. 2, 2013, pp. 994-1002.
IEEE DOI 1307
genetic algorithms; crowding; public transport; train-stop location BibRef

Chen, D., Chen, R., Li, Y., Tang, T.,
Online Learning Algorithms for Train Automatic Stop Control Using Precise Location Data of Balises,
ITS(14), No. 3, 2013, pp. 1526-1535.
IEEE DOI 1309
Balise BibRef

Gao, S., Dong, H., Chen, Y., Ning, B., Chen, G., Yang, X.,
Approximation-Based Robust Adaptive Automatic Train Control: An Approach for Actuator Saturation,
ITS(14), No. 4, 2013, pp. 1733-1742.
IEEE DOI 1312
Actuators BibRef

Wang, H., Yu, F.R., Zhu, L., Tang, T., Ning, B.,
A Cognitive Control Approach to Communication-Based Train Control Systems,
ITS(16), No. 4, August 2015, pp. 1676-1689.
IEEE DOI 1508
Channel models BibRef

Li, L., Dong, W., Ji, Y., Zhang, Z., Tong, L.,
Minimal-Energy Driving Strategy for High-Speed Electric Train With Hybrid System Model,
ITS(14), No. 4, 2013, pp. 1642-1653.
IEEE DOI 1312
Cost function BibRef

Zhang, L., Zhuan, X.,
Braking-Penalized Receding Horizon Control of Heavy-Haul Trains,
ITS(14), No. 4, 2013, pp. 1620-1628.
IEEE DOI 1312
Computational modeling BibRef

Zhao, L.H.[Lin-Hai], Cai, B.G.[Bai-Gen], Xu, J.J.[Jun-Jie], Ran, Y.K.[Yi-Kui],
Study of the Track-Train Continuous Information Transmission Process in a High-Speed Railway,
ITS(15), No. 1, February 2014, pp. 112-121.
IEEE DOI 1403
rail traffic control BibRef

Li, Y.[Ying], Trinh, H.[Hoang], Haas, N.[Norman], Otto, C.[Charles], Pankanti, S.[Sharath],
Rail Component Detection, Optimization, and Assessment for Automatic Rail Track Inspection,
ITS(15), No. 2, April 2014, pp. 760-770.
IEEE DOI 1404
BibRef
Earlier: A2, A3, A1, A4, A5:
Enhanced rail component detection and consolidation for rail track inspection,
WACV12(289-295).
IEEE DOI 1203
Cameras BibRef

Trinh, H.[Hoang], Haas, N.[Norman], Pankanti, S.[Sharath],
Multisensor evidence integration and optimization in rail inspection,
ICPR12(886-889).
WWW Link. 1302
BibRef

Li, Y.[Ying], Otto, C.[Charles], Haas, N.[Norm], Fujiki, Y.C.[Yui-Chi], Pankanti, S.[Sharath],
Component-based track inspection using machine-vision technology,
ICMR11(60).
DOI Link 1301
inspection and condition monitoring of railroad tracks. BibRef

Song, Y., Song, Q., Cai, W.,
Fault-Tolerant Adaptive Control of High-Speed Trains Under Traction/Braking Failures: A Virtual Parameter-Based Approach,
ITS(15), No. 2, April 2014, pp. 737-748.
IEEE DOI 1404
Aerodynamics BibRef

Wang, Y., Song, Y., Gao, H., Lewis, F.L.,
Distributed Fault-Tolerant Control of Virtually and Physically Interconnected Systems With Application to High-Speed Trains Under Traction/Braking Failures,
ITS(17), No. 2, February 2016, pp. 535-545.
IEEE DOI 1602
Couplings BibRef

Liu, Z.[Zhen], Li, F.J.[Feng-Jiao], Huang, B.K.[Bang-Kui], Zhang, G.J.[Guang-Jun],
Real-time and accurate rail wear measurement method and experimental analysis,
JOSA-A(31), No. 8, August 2014, pp. 1721-1729.
DOI Link 1408
Optical instruments; Imaging systems; Machine vision optics BibRef

Molodova, M., Li, Z.L.[Zi-Li], Nunez, A., Dollevoet, R.,
Automatic Detection of Squats in Railway Infrastructure,
ITS(15), No. 5, October 2014, pp. 1980-1990.
IEEE DOI 1410
acceleration measurement BibRef

Ai, B.[Bo], Cheng, X.[Xiang], Kurner, T., Zhong, Z.D.[Zhang-Dui], Guan, K.[Ke], He, R.S.[Rui-Si], Xiong, L.[Lei], Matolak, D.W., Michelson, D.G., Briso-Rodriguez, C.,
Challenges Toward Wireless Communications for High-Speed Railway,
ITS(15), No. 5, October 2014, pp. 2143-2158.
IEEE DOI 1410
data communication BibRef

Kecman, P., Goverde, R.M.P.,
Online Data-Driven Adaptive Prediction of Train Event Times,
ITS(16), No. 1, February 2015, pp. 465-474.
IEEE DOI 1502
Adaptation models BibRef

Hung, R.[Raymond], King, B.[Bruce], Chen, W.[Wu],
Conceptual Issues Regarding the Development of Underground Railway Laser Scanning Systems,
IJGI(4), No. 1, 2015, pp. 185-198.
DOI Link 1502
BibRef

Salmane, H., Khoudour, L., Ruichek, Y.,
A Video-Analysis-Based Railway-Road Safety System for Detecting Hazard Situations at Level Crossings,
ITS(16), No. 2, April 2015, pp. 596-609.
IEEE DOI 1504
Accidents BibRef

Xu, P., Liu, R., Sun, Q., Jiang, L.,
Dynamic-Time-Warping-Based Measurement Data Alignment Model for Condition-Based Railroad Track Maintenance,
ITS(16), No. 2, April 2015, pp. 799-812.
IEEE DOI 1504
Data models BibRef

Lauer, M., Stein, D.,
A Train Localization Algorithm for Train Protection Systems of the Future,
ITS(16), No. 2, April 2015, pp. 970-979.
IEEE DOI 1504
Global Positioning System BibRef

Lu, D., Schnieder, E.,
Performance Evaluation of GNSS for Train Localization,
ITS(16), No. 2, April 2015, pp. 1054-1059.
IEEE DOI 1504
Accuracy BibRef

Elberink, S.O.[Sander Oude], Khoshelham, K.[Kourosh],
Automatic Extraction of Railroad Centerlines from Mobile Laser Scanning Data,
RS(7), No. 5, 2015, pp. 5565-5583.
DOI Link 1506
BibRef

Hodge, V.J., O'Keefe, S., Weeks, M., Moulds, A.,
Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey,
ITS(16), No. 3, June 2015, pp. 1088-1106.
IEEE DOI 1506
Base stations BibRef

Aytekin, C., Rezaeitabar, Y., Dogru, S., Ulusoy, I.,
Railway Fastener Inspection by Real-Time Machine Vision,
SMCS(45), No. 7, July 2015, pp. 1101-1107.
IEEE DOI 1506
Fasteners BibRef

Arastounia, M.[Mostafa],
Automated Recognition of Railroad Infrastructure in Rural Areas from LIDAR Data,
RS(7), No. 11, 2015, pp. 14916.
DOI Link 1512
BibRef

Wu, X.[Xiao], Yuan, P.[Ping], Peng, Q.A.[Qi-Ang], Ngo, C.W.[Chong-Wah], He, J.Y.[Jun-Yan],
Detection of bird nests in overhead catenary system images for high-speed rail,
PR(51), No. 1, 2016, pp. 242-254.
Elsevier DOI 1601
Bird nest detection BibRef

Liu, L., Zhou, F., He, Y.,
Vision-based fault inspection of small mechanical components for train safety,
IET-ITS(10), No. 2, 2016, pp. 130-139.
DOI Link 1602
automatic optical inspection BibRef

Hyde, P., Defossez, F., Ulianov, C.,
Development and testing of an automatic remote condition monitoring system for train wheels,
IET-ITS(10), No. 1, 2016, pp. 32-40.
DOI Link 1602
computational geometry BibRef

Rama, D., Andrews, J.D.,
Railway infrastructure asset management: The whole-system life cost analysis,
IET-ITS(10), No. 1, 2016, pp. 58-64.
DOI Link 1602
Monte Carlo methods BibRef

Gibert, X.[Xavier], Patel, V.M.[Vishal M.], Chellappa, R.[Rama],
Deep Multitask Learning for Railway Track Inspection,
ITS(18), No. 1, January 2017, pp. 153-164.
IEEE DOI 1701
BibRef
Earlier:
Robust Fastener Detection for Autonomous Visual Railway Track Inspection,
WACV15(694-701)
IEEE DOI 1503
BibRef
Earlier:
Sequential Score Adaptation with Extreme Value Theory for Robust Railway Track Inspection,
CVRoads15(131-138)
IEEE DOI 1602
BibRef
Earlier:
Material Classification and Semantic Segmentation of Railway Track Images with Deep Convolutional Neural Networks,
ICIP15(621-625)
IEEE DOI 1512
Detectors. Bayes methods. Deep Convolutional Neural Networks BibRef

Espinosa, F., Hernández, Á., Mazo, M., Ureña, J., Pérez, M.C., Jiménez, J.A., Fernández, I., García, J.C., García, J.J.,
Detector of Electrical Discontinuity of Rails in Double-Track Railway Lines: Electronic System and Measurement Methodology,
ITS(18), No. 4, April 2017, pp. 743-755.
IEEE DOI 1704
Degradation BibRef

Benedetto, F., Tosti, F., Alani, A.M.,
An Entropy-Based Analysis of GPR Data for the Assessment of Railway Ballast Conditions,
GeoRS(55), No. 7, July 2017, pp. 3900-3908.
IEEE DOI 1706
Electronic ballasts, Entropy, Ground penetrating radar, Radar tracking, Rail transportation, Rails, Receiving antennas, Ballast fouling, entropy, ground penetrating radar (GPR), performance analysis, railway, ballast BibRef

Wang, F., Xu, T., Tang, T., Zhou, M., Wang, H.,
Bilevel Feature Extraction-Based Text Mining for Fault Diagnosis of Railway Systems,
ITS(18), No. 1, January 2017, pp. 49-58.
IEEE DOI 1701
Fault diagnosis BibRef

Dai, P.[Peng], Wang, S.C.[Sheng-Chun], Huang, Y.P.[Ya-Ping], Wang, H.[Hao], Du, X.Y.[Xin-Yu], Han, Q.A.[Qi-Ang],
Visual Indexing of Large Scale Train-Borne Video for Rail Condition Perceiving,
IEICE(E100-D), No. 9, September 2017, pp. 2017-2026.
WWW Link. 1709
BibRef

Mao, Z., Tao, G., Jiang, B., Yan, X.G.,
Adaptive Compensation of Traction System Actuator Failures for High-Speed Trains,
ITS(18), No. 11, November 2017, pp. 2950-2963.
IEEE DOI 1711
Actuators, Adaptation models, Adaptive systems, Dynamics, Force, Mathematical model, Resistance, Actuator failures, adaptive control, failure compensation, high-speed train BibRef

Gao, M., Wang, P., Wang, Y., Yao, L.,
Self-Powered ZigBee Wireless Sensor Nodes for Railway Condition Monitoring,
ITS(19), No. 3, March 2018, pp. 900-909.
IEEE DOI 1804
Magnetic levitation, Monitoring, Rail transportation, Rails, Wireless communication, Wireless sensor networks, ZigBee, wireless sensor networks BibRef

Krummenacher, G., Ong, C.S., Koller, S., Kobayashi, S., Buhmann, J.M.,
Wheel Defect Detection With Machine Learning,
ITS(19), No. 4, April 2018, pp. 1176-1187.
IEEE DOI 1804
Force measurement, Learning systems, Rail transportation, Strain measurement, Vibrations, Wavelet transforms, Wheels, wavelet transforms BibRef

Fontul, S.[Simona], Paixão, A.[André], Solla, M.[Mercedes], Pajewski, L.[Lara],
Railway Track Condition Assessment at Network Level by Frequency Domain Analysis of GPR Data,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Fan, H., Cosman, P.C., Hou, Y., Li, B.,
High-Speed Railway Fastener Detection Based on a Line Local Binary Pattern,
SPLetters(25), No. 6, June 2018, pp. 788-792.
IEEE DOI 1806
fasteners, feature extraction, image classification, image sensors, image texture, object detection, railways, center point, visual inspection BibRef

Zhou, F.Q.A.[Fu-Qi-Ang], Song, Y.[Ya], Liu, L.[Liu], Zheng, D.T.[Dong-Tian],
Automated visual inspection of target parts for train safety based on deep learning,
IET-ITS(12), No. 6, August 2018, pp. 550-555.
DOI Link 1807
BibRef

Mao, Q.Z.[Qing-Zhou], Cui, H.[Hao], Hu, Q.W.[Qing-Wu], Ren, X.C.[Xiao-Chun],
A rigorous fastener inspection approach for high-speed railway from structured light sensors,
PandRS(143), 2018, pp. 249-267.
Elsevier DOI 1808
High-speed railway, Fastener inspection, Structured light sensor, Dense point cloud, Decision tree, Centerline extraction BibRef

Wang, H.F.[Hai-Feng], Zhao, N.[Ning], Ning, B.[Bin], Tang, T.[Tao], Chai, M.[Ming],
Safety monitor for train-centric CBTC system,
IET-ITS(12), No. 8, October 2018, pp. 931-938.
DOI Link 1809
BibRef

Lou, Y.D.[Yi-Dong], Zhang, T.[Tian], Tang, J.[Jian], Song, W.W.[Wei-Wei], Zhang, Y.[Yi], Chen, L.[Liang],
A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Zhan, Y., Linb, K., Zhan, H., Guo, Y., Sun, G.,
A Unified Framework for Fault Detection of Freight Train Images Under Complex Environment,
ICIP18(1348-1352)
IEEE DOI 1809
Fault detection, Proposals, Feature extraction, Databases, Task analysis, Joining processes, Fasteners, unified framework, convolutional neural network (CNN) BibRef

Nicodeme, C., Stanciulescu, B.,
Pollution Detection on Rail Surface for Adhesion Evaluation Using Multispectral Images,
DICTA17(1-6)
IEEE DOI 1804
adhesion, image processing, matrix decomposition, pattern clustering, pollution, rails, railway engineering, Surface contamination BibRef

Daoust, T.[Tyler], Pomerleau, F.[François], Barfoot, T.[Timothy],
Light at the End of the Tunnel: High-Speed LiDAR-Based Train Localization in Challenging Underground Environments,
CRV16(93-100)
IEEE DOI 1612
Award, Best Robotics Paper. lidar; localization; mapping; train BibRef