Target Detection and Recognition for Traffic Congestion in Smart Cities Using Deep Learning-Enabled UAVs: A Review and Analysis
Source abstract
Abstract
In smart cities, target detection is one of the major issues in order to avoid traffic congestion. It is also one of the key topics for military, traffic, civilian, sports, and numerous other applications. In daily life, target detection is one of the challenging and serious tasks in traffic congestion due to various factors such as background motion, small recipient size, unclear object characteristics, and drastic occlusion. For target examination, unmanned aerial vehicles (UAVs) are becoming an engaging solution due to their mobility, low cost, wide field of view, accessibility of trained manipulators, a low threat to people’s lives, and ease to use. Because of these benefits along with good tracking effectiveness and resolution, UAVs have received much attention in transportation technology for tracking and analyzing targets. However, objects in UAV images are usually small, so after a neural estimation, a large quantity of detailed knowledge about the objects may be missed, which results in a deficient performance of actual recognition models. To tackle these issues, many deep learning (DL)-based approaches have been proposed. In this review paper, we study an end-to-end target detection paradigm based on different DL approaches, which includes one-stage and two-stage detectors from UAV images to observe the target in traffic congestion under complex circumstances. Moreover, we also analyze the evaluation work to enhance the accuracy, reduce the computational cost, and optimize the design. Furthermore, we also provided the comparison and differences of various technologies for target detection followed by future research trends.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Video Surveillance and Tracking Methods · Autonomous Vehicle Technology and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Engineering, Central South University, Changsha 410083, China
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
Department of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), Miklukho-Maklaya, 117198 Moscow, Russia
Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 Saint Petersburg, Russia
Expertise Centre for Digital Media, Hasselt University, 3500 Hasselt, Belgium
Smart Systems Engineering Laboratory, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia
Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh 33511, Egypt
Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt
Funding
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia · Ministry of Science and High Education of the Russian Federation
References (100+)
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