Deep CNN-based autonomous system for safety measures in logistics transportation - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Soft Computing Année : 2021

Deep CNN-based autonomous system for safety measures in logistics transportation

Abdelkarim Rouari
  • Fonction : Auteur
  • PersonId : 1133556
Hafiz Tayyab Rauf
Seifedine Kadry
  • Fonction : Auteur
  • PersonId : 1133557

Résumé

The careless activity of drivers in logistics transportation is a primary reason inside the vehicle during road accidents. This research aims to reduce the number of accidents caused by a failure of the driver in logistics transportation by incorporating an autonomous system. We propose a convolutional neural network-based architecture to recognize and classify different positions which cause road accidents. The proposed system is evaluated with the State Farm Distracted Driver Database, which included examples illustrating ten different driving positions like reaching behind and talking to the passenger, making up, safe driving, talking on the phone, clothing, checking right/left hand, right/left hand, and running the radio. The proposed approach has also been tested against recent algorithms and evaluated. Our model has obtained 98.98% accuracy compared to other types of approaches with different descriptors and classification techniques
Fichier principal
Vignette du fichier
Rouari2021_Article_DeepCNN-basedAutonomousSystemF_YC.pdf (3.21 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03655714 , version 1 (29-04-2022)

Identifiants

Citer

Abdelkarim Rouari, Abdelouahab Moussaoui, Youssef Chahir, Hafiz Tayyab Rauf, Seifedine Kadry. Deep CNN-based autonomous system for safety measures in logistics transportation. Soft Computing, 2021, 25 (18), pp.12357 - 12370. ⟨10.1007/s00500-021-05949-1⟩. ⟨hal-03655714⟩
32 Consultations
79 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More