Passenger Density Detection in Railway Carriages
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 2024-10-16 through 2024-10-18 -- Ankara -- 204562
Anahtar Kelimeler
crowd analysis, deep learning, density detection, image processing, YOLO
Kaynak
WoS Q Değeri
Scopus Q Değeri
N/A
