Railway Fault Detection and Early Warning System Based on Computer Vision

Because machine vision has good tracking and recognition ability for non-contact surface information, it is widely used in the field of damage detection. Although there are numerous railroad fault detection systems based on machine vision technology, the existing systems are often difficult to combine high detection efficiency with high accuracy. In the face of a huge amount of data detection tasks, the existing railroad fault detection system is difficult to cope with. Therefore, there is an urgent need for more accurate and efficient detection equipment, this paper through a deep learning-based target detection algorithm to design the railroad defect detection and early warning system. The system has the functions of image fusion, image preprocessing and damage recognition, which provides strong technical support for fault detection. The final experimental results show that the accuracy and precision of the algorithm proposed in this paper are as high as 0.95 and 0.97, respectively, and the maximum deviation between the predicted value of the system and the actual value is only 2.5%, which successfully improves the efficiency and precision of the railroad fault detection, and provides a strong guarantee for the safe operation of the railroad.