Before North American trains depart a terminal or rail yard, many aspects of the cars and locomotives undergo inspection, including their safety appliances. Safety appliances are handholds, ladders and other objects that serve as the interface between humans and railcars during transportation. The current inspection process is primarily visual and is labor intensive, redundant, and generally lacks “memory” of the inspection results. The effectiveness and efficiency of safety appliance inspections can be improved by use of machine vision technology. This paper describes a research project investigating the use of machine vision technology to perform railcar safety appliance inspections. Thus far, algorithms have been developed that can detect deformed ladders, handholds, and brake wheels on open-top gondolas and hoppers. Visual learning is being used to teach the algorithm the differences between safety appliance defects that require immediate repair, and other types of deformation that do not. Field experiments under natural and artificial lighting have been conducted to determine the optimal illumination needed for proper functioning of the algorithms. Future work will consist of developing algorithms that can identify deformed safety appliances across the spectrum of North American railcars under varied environmental conditions. The final product will be a wayside inspection system capable of inspecting safety appliance defects on passing railcars.

• J.R. Edwards, J.M. Hart, S. Todorovic, C.P.L. Barkan, N. Ahuja, Z. Chua, N. Kocher, J. Zeman, “Development of Machine Vision Technology for Railcar Safety Appliance Inspection,” Proc. International Heavy Haul Conference (IHHA), Specialist Technical Session, Kiruna, Sweden, June 2007, 745-752.