Using Images to Identify Things and Assets in Railway systems

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Machine vision (object recognition) is the ability for recognizing images and to understand what is seen. It involves digital cameras, digital signal processing and a machine learning algorithm. After the image is taken - the particular steps within machine vision include:

Image processing - stitching, filtering, pixel counting.

Segmentation - partitioning the image into multiple segments to simplify and/or change the representation of the image into something that is meaningful and easier to analyze.

Blob checking - checking the image for discrete spots of connected pixels (e.g. a black area in a grey object) as image landmarks. These blobs frequently represent optical targets for observation, robotic capture, or manufacturing failure.

Pattern recognition algorithm including template matching, i.e. finding and matching with specific patterns using some ML method (neural network, deep learning etc.). Re-positioning of the object may be required, or varying in size.

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Railway Use Cases

To maintain the railway, it is important to automatically inspect different assets and components, such as connections among railway tracks, and contour poles supporting power cables. Tracks can be damaged by the friction between their surfaces and wheels. Electrification systems, such as overhead power lines, should be periodically checked since they should stably supply electric power to the locomotive.

However, traditional inspection systems are always dependent on human manual observation, which is not quite efficient, since human observers become easily tired and lose focus on important objects after a few minutes. Also, only a small time interval is allowed for human inspection, since trains usually operate almost all day.

To solve those problems, various image processing and computer vision-based automatic inspection approaches have been introduced. The new

The Machine Vision Approaches

The new AI/ ML-based techniques, such as computer vision and image recognition can be applied?to inspect defects in railways for safety and maintenance, and it is called image-based railway inspection system (IRIS). Here we will list some significant applications of machine vision (object recognition) in railway transport:

In 2017, Amaral and his colleagues presented a system for obstacle detection in railway level crossings - by a set of points obtained from curved 2D laser scanners.

Kim and Cohn (2004) set up a camera in front of a locomotive to investigate the level crossing traffic accidents. They developed a computer vision system that automatically detects the possible after-accident scenes by detecting the shape of the vehicles passing in front of the train.

Kantor and colleagues, for maintenance purposes, applied a laser light line to generate a 3-D profile of the railroad surface, and a ground penetrating radar to obtain subsurface measurements.

For visual inspection - Rubinsztejn and Chen used cameras to acquire real images. In order to achieve the automatic detection of parts of interest, missing elements or defects, they processed the captured images with pattern recognition algorithms.

Singh et al. (2006) used image processing methods, such as edge detection and color analysis, to detect missing clips.

Deutschl et al. (2004) used convolution filters and morphological image analysis to detect rail surface defects.

Weil combined a ground penetrating radar with infrared imaging systems to detect subsurface defects in railroad track beds.

The hardware and software for machine vision have improved dramatically in recent years, so its applications in railway technology will increase in the upcoming years.?