Machine vision-based portable track inspection system

Abstract

With the rapid development of the transportation industry, railway transportation plays a crucial role. Manual inspection methods are time-consuming, labor-intensive, and highly subjective. Therefore, there is an urgent need for a more efficient and accurate flaw detection method. This system is a portable rail flaw detection device based on machine vision, with YOLOv5 as its core deep learning algorithm. The system captures surface images of the rail through a camera and transmits them in real-time to the host computer for analysis. Leveraging the powerful real-time object detection capability of YOLOv5s, the system can accurately identify and locate various types of rail surface damages, such as cracks, fractures, and wear. Compared to traditional manual inspection, this system is more efficient and greatly improves the accuracy and efficiency of rail flaw detection. It has a smaller size and is convenient to carry, making it suitable for working in various environments and conditions, greatly enhancing the practicality and flexibility of the device.