Title :

SNCF workers detection in the railway environment based on improved YOLO v5

Author(s) :

Hathat, Yahia [Auteur]
Université Kasdi Merbah Ouargla
Samai, Djamel [Auteur]
Université Kasdi Merbah Ouargla
Benlamoudi, Azeddine [Auteur]
Université Kasdi Merbah Ouargla
Center for Machine Vision Research [CMV]
Bensid, Khaled [Auteur]
Université Kasdi Merbah Ouargla
Taleb-Ahmed, Abdelmalik [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]

Conference title :

2022 7th International Conference on Image and Signal Processing and their Applications (ISPA)

City :

Mostaganem

Country :

Algérie

Start date of the conference :

2022-05-08

Journal title :

SNCF workers detection in the railway environment based on improved YOLO v5

Publisher :

IEEE

Publication date :

2022-06-03

English keyword(s) :

convolutional neural network
object detection
YOLO v5
SNCF workers

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

In nearest past years object detection techniques becomes the magic key to solving several problems in computer vision, in this work, we introduce our enhanced YOLO v5 detector for detecting SNCF (National Society of France ...
Show more >In nearest past years object detection techniques becomes the magic key to solving several problems in computer vision, in this work, we introduce our enhanced YOLO v5 detector for detecting SNCF (National Society of France Railroad) workers in the railway environment. Our contribution in this work is presented by creating a new dataset about SNCF workers to use for training our model detector and improving YOLO v5 by reducing the number of its parameters where we reduce the number of classes in YOLO layers to only one class, that ensure to augment the speed of detection and increase the accuracy of our detector. Finally, we apply the four versions of YOLO v5 (S, M, L, X) and compare them. We achieved a high speed in the detection of SNCF workers in YOLO v5-S with 0.1 ms and high precision in YOLO v5-X with a rate of 0.9731 %.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL