TTCI R&D: Machine Learning for Machine Vision Systems - Railway Age

RAILWAY AGE, JULY 2021 ISSUE: Reliable, real-time monitoring of in-service railcar components will enhance the potential for maintenance planning. Through the Association of American Railroads (AAR) Strategic Research Initiatives (SRI) program, Transportation Technology Center, Inc. (TTCI) has been assisting suppliers and other stakeholders in the development of machine vision technologies and related algorithms for evaluating railcar components and conditions. To enhance safety and reduce worker exposure to yard risk, North American railroads have begun to install machine vision inspection systems in revenue service. Using commercially available deep learning system platforms, TTCI researchers developed and demonstrated three convolutional neural network-based applications for analyzing visual images. A convolutional neural network is a type of artificial neural network that uses machine learning algorithms to analyze digital images. Convolutional neural networks are more powerful and effective than traditional artificial neural networks at recognizing, interpreting, and categorizing large, unstructured data sets; particularly those comprised of visual imagery.