In recent decades, 3D reconstruction techniques have been applied in an increasing number of areas such as virtual reality, robot navigation, medical imaging and architectural restoration of cultural relics. Most of the inspection techniques used in railway systems are, however, still implemented on a 2D basis. This is particularly true of track inspection due to its linear nature. Benefiting from the development of sensor technology and constantly improving processors, higher quality 3D model reconstructions are becoming possible which push the technology into more challenging areas. One such advancement is the use of 3D perceptual techniques in railway systems. This paper presents a novel 3D perceptual system, based on a low-cost 2D laser sensor, which has been developed for the detection and characterisation of physical surface defects in railway tracks. An innovative prototype system has been developed to capture and correlate the laser scan data; dedicated 3D data processing procedures have then been developed in the form of three specific defect-detection algorithms (depth gradient, face normal and face-normal gradient) which are applied to the 3D model. The system has been tested with rail samples in the laboratory and at the Long Marston Railway Test Track. The 3D models developed represent the external surface of the samples both laterally (2D slices) and longitudinally (3D model), and common surface defects can be detected and represented in 3D. The results demonstrate the feasibility of applying 3D reconstruction-based inspection techniques to railway systems.
|Number of pages||19|
|Journal||Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit|
|Early online date||31 Aug 2018|
|Publication status||Published - 1 Mar 2019|
- condition monitoring
- defect detection
- 3D model