SNCF workers detection in the railway ...
Type de document :
Communication dans un congrès avec actes
Titre :
SNCF workers detection in the railway environment based on improved YOLO v5
Auteur(s) :
Hathat, Yahia [Auteur]
Université Kasdi Merbah Ouargla
Samai, Djamel [Auteur]
Université Kasdi Merbah Ouargla
Benlamoudi, Azeddine [Auteur]
Center for Machine Vision Research [CMV]
Université Kasdi Merbah Ouargla
Bensid, Khaled [Auteur]
Université Kasdi Merbah Ouargla
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Université Kasdi Merbah Ouargla
Samai, Djamel [Auteur]
Université Kasdi Merbah Ouargla
Benlamoudi, Azeddine [Auteur]
Center for Machine Vision Research [CMV]
Université Kasdi Merbah Ouargla
Bensid, Khaled [Auteur]
Université Kasdi Merbah Ouargla
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Titre de la manifestation scientifique :
2022 7th International Conference on Image and Signal Processing and their Applications (ISPA)
Ville :
Mostaganem
Pays :
Algérie
Date de début de la manifestation scientifique :
2022-05-08
Titre de la revue :
SNCF workers detection in the railway environment based on improved YOLO v5
Éditeur :
IEEE
Date de publication :
2022-06-03
Mot(s)-clé(s) en anglais :
convolutional neural network
object detection
YOLO v5
SNCF workers
object detection
YOLO v5
SNCF workers
Discipline(s) HAL :
Sciences de l'ingénieur [physics]
Résumé en anglais : [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 ...
Lire la suite >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 %.Lire moins >
Lire la suite >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 %.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Source :