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Figure 8. Image acquisition setup.
Figure 9. A sample image for training.
We worked with rail maintenance operations staff to describe and label the images according to their
properties. Figure 10 shows some sample patterns of “normal”, “corrugation”, “insulated rail joint”, and
“weld” images.
5.2. Deep learning environment configurations
The deep learning experiments were performed on a platform with OS (Windows or Linux) and GPU. Our
configurations include Intel Core i7-8700 CPU, Nvidia GeForce RTX 2080 Ti GPU, and 64 GB RAM.
Software packages such as Python 3.6, Nvidia CUDA Toolkit, cuDNN, and TensorFlow with GPU support
were installed on Windows 10 operating system for our experiments.
5.3. Application 1: CNN
We conducted training and prediction experiments for both anomaly detection and classification purpose.
The input images were categorized by the neural network into two output classes for detection tasks and ten
output classes for classification tasks. For both tasks, 90% of the image samples were randomly reserved for
