Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

作者：Michael Abebe Berwo, Asad Khan, Yong Fang, H. Fahim, Shumaila Javaid, Jabar Mahmood, Z. Abideen, Syam Melethil Sethumadhavan · 发表于：Italian National Conference on Sensors · 年份：2023 · DOI：10.3390/s23104832 · 被引用次数：72 · 研究领域：Medicine、Computer Science

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.