@article {Fakhari2021867,
	title = {A Comparative Study Between the Most Usable Object Detection Methods Based on Deep Convolutional Neural Networks},
	journal = {Lecture Notes in Networks and Systems},
	volume = {183},
	year = {2021},
	note = {cited By 0},
	pages = {867-876},
	abstract = {Object detection is a computer vision technique that has been revolutionized by the rapid development of convolutional neural network architectures. These networks consist of powerful tools, able to learn and extract high-level features more complex. They are introduced to deal with the problems existing in traditional architectures, to find and characterize a large number of objects in an image. This technique has two types of detection: a simple detection that aims to identify a single object in an image, it is a classification problem. And multiple detections that aim not only to identify all the objects in the image but also to find the location of the objects. This article describes a simple summary of datasets and deep learning algorithms commonly used in object detection. {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-030-66840-2_66},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102654084\&doi=10.1007\%2f978-3-030-66840-2_66\&partnerID=40\&md5=cd77d1e63cb0e5eb20f6c381c74915c4},
	author = {Fakhari, A. and Lazaar, M. and Omara, H.}
}
