U-Net (V2) Based Railway Track Crack Detection & Risk Analysis using Computer Vision

Railroad transportation is an essential component of modern transportation infrastructure, and the preservation of the structural state of the railroad tracks is critical for the reliable and safe operation of railroad infrastructure. The continuous mechanical load, fatigue of materials, environmental factors and prolonged use may cause cracks to appear on the surface of the railroad tracks. In case when cracks remain undetected in the early stages, this may result in infrastructure degradation, high maintenance cost, downtime and even safety issues. The conventional railway track inspection process is done through visual manual inspections, which are time-consuming, error-prone and labour-intensive. In this work we present an approach to detect cracks on railroad tracks and assess the risks with the help of deep learning U-Net V2 algorithm.As an input image of the railroad tracks is provided. Following the preprocessing stage, the system uses the U-Net V2 model for crack segmentation. Then, the crack positions are detected through the use of the U-Net V2 model pixel by pixel. At the last stage, the system analyzes the crack features for the severity of the damage. Lastly, the system classifies the risk level of the cracks as either low, medium, or high, helping the maintenance crews prioritize their work. The proposed system can be classified as an automatic inspection system. The main purpose of such a system is to reduce the need for manual inspection of railroad tracks by human operators. In addition, it is aimed to assist in maintenance. That is, detection of potential defects before they cause any problem. Such an approach allows for the inspection of items overtime and detection of warning signs of any malfunction that might occur. Thus, repairing can be performed beforehand not in a rush manner. Fewer interruptions, lower costs, security, etc. The system is completely autonomous. However, people still check whether the system functions correctly. This is a part of the equipment maintenance process.This new method utilizes Railroad Track Surface Faults Dataset. The data set consists of 5250 pictures of the railroad track with surface faults. The system is designed on such technologies as Python, OpenCV, Tensorflow, Keras, Numpy, Pandas, Scikit-learn, and Matplotlib. Such parameters will be used to measure the performance of the system in terms of image segmentation and classification: IoU, Dice coefficient, precision, recall, F1 score and accuracy.