Lightweight CNNs-Based Object Detection forEmbedded Systems implementation

Type : Article de conférence
Auteur(s) :  BOUGUETTAYA Abdelmalek, Kechida Ahmed, TABERKIT Mohammed Amine
Année :  2019
Domaine : Informatique
Conférence: 1st Conference on Innovative Trends in Computer Science (CITCS'2019)
Lieu de la conférence:  Guelma, Algeria
Résumé en PDF :  (résumé en pdf)
Fulltext en PDF :  (.pdf)
Mots clés :  Computer vision, Deep Learning, Object Detection, Convolutional Neural Network, lightweight CNN

Résumé : 

Deep Learning algorithms, based on the implementation of Convolutional Neural Networks (CNN), are more and more used in Artificial Intelligence (AI) applications, especially in the image recognition field, like image classification, object detection, segmentation. These algorithms learn from training data a set of parameters to create a model, which is capable of performing a classification task with high accuracy. The most recent models consist of millions of parameters, which make it computationally very exhausting, especially in the field of embedded systems where resources are very limited. Recently, deep learning and computer vision are highly used to realize a fully-autonomous drone and self-driving cars, which does not need human intervention. Computer vision is a field focused on enabling drones to interpret and understand the content of an image or a video using CNNs. This paper focuses on reviewing recent lightweight CNNs architectures used that can be implemented on embedded targets.