A laser scanner based approach for identifying rail surface squat defects

Abstract

The defect identification process within the UK rail industry has seen significant improvements over the past decade with the introduction of new measurement systems and defect detection systems. Although significant work has been on the defect identification little work has been done on the process after the defect has been detected. This repair process is still extremely manual. Due to the current process being manual the repair operation has very little traceability and transparency. This paper has therefore presented the need for not only a defect detection system but a defect repair system for the UK railway industry. Further to this, this paper has acknowledged that the rise of defects occurring on the UK railway lines requires a solution that can fully repair a defect with little to no user intervention in a timely manner. To address this, this paper has taken the extremely manual process of rail repair and has laid out the possibilities to automate this process. By doing this a work flow diagram has been generated to show how the system could be used to repair surface defects with a specific focus being made on squat defects. To achieve this a defect detection and measurement system has been explored, as this will make up the first stage of the automated repair system. The literature on various defect detection algorithms was reviewed and two variations of existing defect detection algorithms were created, i.e. the Covariance method and the Normal Intersection method. These algorithms have been tested against 100 simulated squat defects and have been verified using 4 experimentally generated defects. Both algorithms have been proven to not only identify the approximate size of the defect but also its location. This successful defect identification will be integrated into an automated rail repair system.

Ensemble model for rail surface defects detection

“…Early optical cameras can take images of good quality only with low running speed during the day [ 20 ]. Later laser line scan cameras have overcome this shortcut, they can take photographs under various conditions and can suppress specular reflections [ 21 , 22 ]. More accurate detection results can be achieved by introducing extra information, e.g., 3-D information [ 23 ].…”

BIM and CCD imaging based visual inspection method for airport road surface defects

“…Regular maintenance and repair is the key to ensure the safe operation of the airport. At present, some progress has been made in the algorithm of disease detection for highway pavement, but there is little research on the problem of disease detection for airport pavement [1]. At present, the airport still adopts the pavement disease detection algorithm based on manual inspection, which is time-consuming, laborious, subjective and costly, and cannot meet the needs of the rapid development of the airport.…”

Wayside Railway Switch and Crossing Monitoring Using Isolation Forest Anomaly Scores

“…In one study, Lesiak et al simplified a technique for detecting squat defects using laser scatterometry, which was able to detect both artificial and real squats [25]. De Becker et al [26] implemented the covariance method and the normal intersection method to identify the defects and their locations. Nonetheless, the laser transmitter experiences comparable issues to those encountered by the camera lens.…”