Computer Vision and Machine Learning Method for Detection and Assessment of Wheel Anomalies Using Sensor Fusion of Thermal and Visible Spectrum Cameras

Dr Timothy Havens The main objective of this research project is to develop computer vision and machine learning methods for automatically detecting the defects of rail car wheels using thermal and visible spectrum (color) camera sensors. The project will concentrate on identifying flat wheels which are a serious concern for both wheel and suspension hardware, and also rail and track structure. Recently, Union Pacific (UP) deployed a thermal imaging sensor that collects images of wheels on a notoriously problematic downhill grade which is known to cause wheel problems due to skidding. The first phase of the project will focus on algorithm development for autonomously detecting and scoring anomalous wheel conditions using thermal images provided by UP. UP has provided Dr. Havens with more than 100,000 images from their thermal sensor, incorporating various types of rail cars.

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