Enhanced railway monitoring and segmentation using DNet and mathematical methods

Abstract

This study explores enhancing security and automation in railway transportation by evaluating the BiSeNetV2, YOLO, and DNet models for railway monitoring and segmentation. Tests were conducted in the Gazebo simulation environment and the field using the Anafi4K UAV, comparing the effectiveness of different algorithms. Additionally, the role of mathematical methods, such as Bezier curves and Bernstein polynomials, in supporting the autonomous flight capabilities of UAVs was examined. These methods have proven effective in helping UAVs follow railway lines by increasing maneuverability, contributing to successful flights. The combination of the BiSeNetV2 model, YOLO models, and these mathematical methods offers a robust solution for railway monitoring and segmentation. Future advancements and broader adoption of these technologies in industrial applications could enhance the safety and efficiency of rail transport. Furthermore, the developed DNet model demonstrated a 99% accuracy rate in segmenting foreign objects around railway lines, proving to be a significant alternative among deep learning models for railway monitoring and segmentation. The model's performance highlights its potential to provide crucial solutions for security and automation in railway transportation.