Roger G. Nyberg

Machine vision approach for automating vegetation detection on railway tracks

Abstract The presence of vegetation on railway tracks (amongst other issues) threatens track safety and longevity. However, vegetation inspections in Sweden (and elsewhere in the world) are currently being carried out manually. Manually inspecting vegetation is very slow and time consuming. Maintaining an even quality standard is also very difficult. A machine vision-based approach is therefore proposed to emulate the visual abilities of the human inspector. Work aimed at detecting vegetation on railway tracks has been split into two main phases. The first phase is aimed at detecting vegetation on the tracks using appropriate image analysis techniques. The second phase is aimed at detecting the rails in the image to determine the cover of vegetation that is present between the rails as opposed to vegetation present outside the rails. Results achieved in the current work indicate that the machine vision approach has performed reasonably well in detecting the presence/absence of vegetation on railway tracks when compared with a human operator.

Reliability of Manual Assessments in Determining the Types of Vegetation on Railway Tracks

Current day vegetation assessments within railway maintenance are (to a large extent) carried out manually. This study has investigated the reliability of such manual assessments by taking three non-domain experts into account. Thirty-five track images under different conditions were acquired for the purpose. For each image, the raters’ were asked to estimate the cover of woody plants, herbs and grass separately (in %) using methods such as aerial canopy cover, aerial foliar cover and sub-plot frequency. Visual estimates of raters’ were recorded and analysis-of-variance tests on the mean cover estimates were investigated to see whether if there were disagreements between the raters’. Intra-correlation coefficient was used to study the differences between the estimates. Results achieved in this work revealed that seven out of the nine analysis-of-variance tests conducted in this study have demonstrated significant difference in the mean estimates of cover (p < 0.05).

Smart Parking Tools Suitability for Open Parking Lots: A Review.

Parking a vehicle in traffic dense environments is a common issue in many parts of the world which oftenleads to congestion and environmental pollution. Lack of guidance information to vacant parki ...

Inter-rater Reliability in Determining the Types of Vegetation on Railway Trackbeds

Vegetation growing on railway trackbeds and embankments can present several potential problems. Consequently, such vegetation is controlled through various maintenance procedures. In order to investigate the extent of maintenance needed, one of the first steps in any maintenance procedure is to monitor or inspect the railway section in question. Monitoring is often carried out manually by sending out inspectors or by watching recorded video clips of the section in question. To facilitate maintenance planning, the ability to assess the extent of vegetation becomes important. This paper investigates the reliability of human assessments of vegetation on railway trackbeds.