Railway Fastener Fault Detection using YOLOv5

Abstract: The railway system is an important part of the sociotechnical society, as it enables efficient, reliable, and sustainable transportation of both people and goods. Despite increasing investments, the Swedish railway has encountered structural and technical problems due to worn-out infrastructure as a result of insufficient maintenance. Two important technical aspects of the rail are the stability and robustness. To prevent transversal and longitudinal deviations, the rail is attached to sleepers by fasteners. The fasteners’ conditions are therefore crucial for the stability of the track and the safeness of the railway. Automatic fastener inspections enable efficient and objective inspections which are a prerequisite for a more adequate maintenance of the railway. This master thesis aims to investigate how machine learning can be applied to the problem of automatic fastener fault detection. The master thesis includes the complete process of applying and evaluating machine learning algorithms to the given problem, including data gathering, data preprocessing, model training, and model evaluation. The chosen model was the state-of-the-art object detector YOLOv5s. To assess the model’s performance and robustness to the given problem, different settings regarding both the dataset and the model’s architecture in terms of transfer learning and hyperparameters were tested.  The results indicate that YOLOv5s is an appropriate machine learning algorithm for fastener fault detection. The models that achieved the highest performance reached an mAP[0.5:0.95] above 0.744 during training and 0.692 during testing. Furthermore, several combinations of different settings had a positive effect on the different models’ performances.  In conclusion, YOLOv5s is in general a suitable model for detecting fasteners. By closer analysis of the result, the models failed when both fasteners and missing fasteners were partly visible in the lower and upper parts of the image. These cases were not annotated in the dataset and therefore resulted in misclassification. In production, the cropped fasteners can be reduced by accurately synchronizing the frequency of capturing data with the distance between the sleepers, in such a way that only one sleeper and corresponding fasteners are visible per image leading to more accurate results. To conclude, machine learning can be applied as an effective and robust technique to the problem of automatic fastener fault detection.