Filter-based feature selection for rail defect detection

Mandriota, C.; Nitti, Massimiliano; Ancona, Nicola; Stella, Ettore; Distante, A.

doi:10.1007/s00138-004-0148-3

Cited by 110 publications

(70 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our survey on ceramic tile inspection revealed that two approaches were dominant, namely approaches that perform the identification of the visual defects using either morphological techniques [1,[12][13][14][15][16][17] or inspection solutions based on texture analysis [2,[18][19][20][21][22][23]. From these approaches, the morphological-based implementations proved prevalent as they offer efficient implementations that are capable of realtime operation.…”

Section: $2 Related Vision-based Inspection Systemsmentioning

confidence: 99%

A Machine Vision System for Quality Grading of Painted Slates

Ghita

Carew

Whelan

2012

Machine Vision Handbook

View full text Add to dashboard Cite

The major aim of this chapter is to detail the technology associated with a novel industrial inspection system that is able to robustly identify the visual defects present on the surface of painted slates. The development of a real-time automated slate inspection system proved to be a challenging task since the surface of the slate is painted with glossy dark colours, the slate is characterised by depth profile non-uniformities and it is transported at the inspection line via high-speed conveyors. In order to implement an industrial compliant system, in our design we had to devise a large number of novel solutions including the development of a full customised illumination set-up and the development of flexible image-processing procedures that can accommodate the large spectrum of visual defects that can be present on the slate surface and the vibrations generated by the slate transport system. The developed machine vision system has been subjected to a thorough robustness evaluation and the reported experimental results indicate that the proposed solution can be used to replace the manual procedure that is currently used to grade the painted slates in manufacturing environments.

show abstract

Section: $2 Related Vision-based Inspection Systemsmentioning

confidence: 99%

A Machine Vision System for Quality Grading of Painted Slates

Ghita

Carew

Whelan

2012

Machine Vision Handbook

View full text Add to dashboard Cite

show abstract

“…Afterwards, it determines mean and variance of the obtained filter responses and uses them as features input to the SVM Classifier Block which produces the final report about the status of the rail. More details on the implemented methods were given in [5]. Fig.…”

Section: System Overviewmentioning

confidence: 99%

“…In other words, (5) means that the set of projections of the input data on u q has variance higher than that one of the set of projections of the input data on u q+1 .…”

Section: Pca-based Data Reduction Stagementioning

confidence: 99%

A Visual Inspection System for Rail Detection and Tracking in Real Time Railway Maintenance

Ruvo¹,

Ruvo²,

Distante³

et al. 2008

TOCSJ

Self Cite

View full text Add to dashboard Cite

Rail inspection is an essential task in railway maintenance and is periodically needed. Inspection is manually operated by trained human operator walking along the track searching for visual anomalies. This monitoring is unacceptable for slowness and lack of objectivity. This paper deals with a patented Visual Inspection System for Railway maintenance, devoted to different tasks. Here, its Rail Detection & Tracking Block (RD&TB) is presented.RD&TB detects and tracks, into the acquired video sequence the rail head, by this way, notably reducing the area to be analyzed and inspected by other modules of VISyR. Thanks to its hardware implementation, RD&TB performs its task in 5.71 μs with an accuracy of 98.5%, allowing an on-the-fly analysis of a video sequence acquired up at 190 km/h. RD&TB is highly flexible and configurable, since it is based on classifiers that can be easily reconfigured in function of different type of rails.

show abstract

“…by rail grinding) they grow further resulting higher maintenance costs. Among several automated inspection systems, visual inspection using video cameras has become more popular within the past few years: Li and Ren (2012);De Ruvo et al (2008); Mandriota et al (2004), mostly because of its simplicity and accessibility. With other inspections systems, such as physical measurements (e.g.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Rail Defect Detection from Imbalanced Image Data

2016

View full text Add to dashboard Cite

Rail defect detection by video cameras has recently gained much attention in both academia and industry. Rail image data has two properties. It is highly imbalanced towards the non-defective class and it has a large number of unlabeled data samples available for semisupervised learning techniques. In this paper we investigate if positive defective candidates selected from the unlabeled data can help improve the balance between the two classes and gain performance on detecting a specific type of defects called Squats. We compare data sampling techniques as well and conclude that the semi-supervised techniques are a reasonable alternative for improving performance on applications such as rail track Squat detection from image data.

show abstract

Filter-based feature selection for rail defect detection

Cited by 110 publications

References 7 publications

A Machine Vision System for Quality Grading of Painted Slates

A Machine Vision System for Quality Grading of Painted Slates

A Visual Inspection System for Rail Detection and Tracking in Real Time Railway Maintenance

Semi-supervised Rail Defect Detection from Imbalanced Image Data

Contact Info

Product

Resources

About