Application of Neural Networks for Object Recognition in Railway Transportation

Abstract

Purpose: With the help of vision systems and neural networks, such as YOLOv8 and MASK R-CNN, it is possible to quickly and accurately detect objects that can lead to an accident or delay trains. YOLOv8 is one of the most popular real-time object detection algorithms that uses deep neural networks to classify and localize objects. YOLOv8 can detect objects in images and videos with high speed and accuracy. This model can work on various hardware platforms, including mobile devices and computers. MASK R-CNN is an even more advanced object detection algorithm that has the ability to highlight objects and their contours with high accuracy. MASK R-CNN uses convolutional neural networks and mask segmentation techniques to detect objects. It can work both in real time and on static images. When vision systems are equipped with YOLOv8 and MASK R-CNN neural networks, they can quickly respond to extraneous objects that appear on the rails. The purpose of the article is to develop algorithms for detecting railway transport objects and obstacles using technical vision and neural networks, and to evaluate the effectiveness of algorithms. Methods: The YOLOv8 algorithm is based on the architecture of convolutional neural networks and uses supervised learning methods. This model takes an image as input and provides estimates of the probability that a certain object is present in the image in real time. To achieve this, YOLOv8 employs region of interest (ROI) detection methods, allowing to determine the areas of the image on which objects may be located. The MASK R-CNN algorithm uses more sophisticated methods, such as mask segmentation methods and proportional resizing of the area of interest (RoIAlign) to achieve more accurate results of object detecting in images and videos. It is also based on convolutional neural networks and uses supervised learning methods. MASK R-CNN uses mask segmentation methods to determine the contour of an object in an image, as well as the RoIAlign method, which allows for superior quality when processing various image sizes. Common mathematical methods that are used in YOLOv8 and MASK R-CNN are methods of convolutional neural network, supervised learning and optimization of the loss function. They are based on deep learning algorithms such as stochastic gradient descent and backward propagation of errors. Results: An algorithm for detecting foreign objects on the route of rolling stock using a technical vision system, calculation of the evaluation of the quality of neural networks performance, error matrices have been formed, the results of neural network processing have been obtained. Practical significance: An algorithm for detecting foreign objects on the route of the moving rolling stock using a technical vision system has been developed, two neural networks have been trained to detect railway transport objects and obstacles on the way.