@article{ref1,
title="Deep learning techniques for vehicle detection and classification from images/videos: a survey",
journal="Sensors (Basel)",
year="2023",
author="Berwo, Michael Abebe and Khan, Asad and Fang, Yong and Fahim, Hamza and Javaid, Shumaila and Mahmood, Jabar and Abideen, Zain Ul and M s, Syam",
volume="23",
number="10",
pages="e4832-e4832",
abstract="Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s23104832",
url="http://dx.doi.org/10.3390/s23104832"
}