Passenger Density Detection in Railway Carriages

Abstract

In this study, the goal is to contribute to reducing waiting times and to improve passanger comfort with passenger density estimation using deep learning methods. For this purpose, the YOLOv8 deep learning model was used to detect passenger density in urban rail systems. The model is trained with the CrowdHuman dataset. The trained model runs on a Raspberry Pi 5 and processes images obtained from IP cameras. These processed images are stored in an SQL Server via an API and the density estimation results are displayed on an LCD screen. This design aims to make the system feasible in the field in terms of performance and cost. The trained model, detects the passenger occupancy with a test accuracy rate of up to 90% and offers significant advantages in real-time applications due to its low computational power requirements. © 2024 IEEE.