Railway track inspection and fault detection using autonomous robotic vehicles

In this study, an improved method for inspection of railway track crack detection is proposed. Because safety maintenance and inspection are crucial for ensuring efficient operations. Traditional methods of inspection often rely on manual labour, which can be time-consuming, costly, and prone to human error. To address these challenges, this paper proposes an innovative approach leveraging autonomous robotic vehicles and deep learning techniques for railway track inspection and fault detection. An infrared (IR) sensor, ultrasonic sensor, acoustic sensor, microcontroller and also LiDAR sensor are used for a wide range of detection. The initial focus was on six specific types of track faults: wheel burns, loose nuts and bolts, damaged sleepers, track creep, low joints, and issues with points and crossings. The extracted features are fed into a deep learning neural network to distinguish between cracked and non-cracked track images. Expected Results are obtained with the help of image processing and convolutional neural network and improved YOLOv5 algorithm with accuracy of 92.9% and an error rate of 1.5%. The railway inspection system uses a unique combination of autonomous robotic vehicles and deep learning, setting it apart from traditional methods.