Smart City Gnosys

Smart city article details

Title	Performance Evaluation And Analysis Of Drone-Based Vehicle Detection Techniques From Deep Learning Perspective
ID_Doc	41727
Authors	Bisio I.; Haleem H.; Garibotto C.; Lavagetto F.; Sciarrone A.
Year	2022
Published	IEEE Internet of Things Journal, 9, 13
DOI	http://dx.doi.org/10.1109/JIOT.2021.3128065
Abstract	From smart cities development perspective, road vehicle detection exploiting drone-based aerial imagery is a crucial part of traffic surveillance and monitoring systems where effective results are of utmost demand. A recent boom in the field of deep learning (DL) has provided remarkable development in the problem of vehicle detection. Aerial views pose more complexity with respect to the ground view but the rapid advancement in the field of DL, the volume of data, and hardware configuration has facilitated the realization of these intelligent detection systems effectively. In this article, a detailed performance evaluation of some of the main state-of-the-art DL-based object detection techniques has been carried out along with an experimental analysis of vehicle detection using the RetinaNet framework on the VisDrone-benchmark data set. The performance of the RetinaNet framework has been validated together with the results provided by the VisDrone team. Further experiments are then conducted to investigate the impact of various parameters. Finally, the selection of suitable models that can be practically implemented is also discussed based both on a qualitative and quantitative analysis. © 2014 IEEE.
Author Keywords	Aerial view; deep learning; drones; traffic monitoring; vehicle detection

Similar Articles

Id	Similarity	Authors	Title	Published
5465	0.903	Bisio I.; Garibotto C.; Haleem H.; Lavagetto F.; Sciarrone A.	A Systematic Review Of Drone Based Road Traffic Monitoring System	IEEE Access, 10 (2022)
51478	0.902	Othman K.M.; Alzaben N.; Alruwais N.; Maray M.; Darem A.A.; Mohamed A.	Smart Surveillance: Advanced Deep Learning-Based Vehicle Detection And Tracking Model On Uav Imagery	Fractals, 33, 2 (2025)
18076	0.897	Zhu S.; Yang K.	Deep Remote Sensing Object Detection For Smart City Applications	Proceedings - 2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024 (2024)
54411	0.88	Iftikhar S.; Asim M.; Zhang Z.; Muthanna A.; Chen J.; El-Affendi M.; Sedik A.; Abd El-Latif A.A.	Target Detection And Recognition For Traffic Congestion In Smart Cities Using Deep Learning-Enabled Uavs: A Review And Analysis	Applied Sciences (Switzerland), 13, 6 (2023)
27308	0.867	Ahmed M.W.; Adnan M.; Ahmed M.; Janssens D.; Wets G.; Ahmed A.; Ectors W.	From Stationary To Nonstationary Uavs: Deep-Learning-Based Method For Vehicle Speed Estimation	Algorithms, 17, 12 (2024)
4512	0.863	Moshayedi A.J.; Roy A.S.; Taravet A.; Liao L.; Wu J.; Gheisari M.	A Secure Traffic Police Remote Sensing Approach Via A Deep Learning-Based Low-Altitude Vehicle Speed Detector Through Uavs In Smart Cites: Algorithm, Implementation And Evaluation	Future Transportation, 3, 1 (2023)
21083	0.863	Sun Y.; Cao B.; Zhu P.; Hu Q.	Drone-Based Rgb-Infrared Cross-Modality Vehicle Detection Via Uncertainty-Aware Learning	IEEE Transactions on Circuits and Systems for Video Technology, 32, 10 (2022)
33790	0.861	Ahmed I.; Ahmad M.; Chehri A.; Hassan M.M.; Jeon G.	Iot Enabled Deep Learning Based Framework For Multiple Object Detection In Remote Sensing Images	Remote Sensing, 14, 16 (2022)
35250	0.861	Momin M.A.; Junos M.H.; Mohd Khairuddin A.S.; Abu Talip M.S.	Lightweight Cnn Model: Automated Vehicle Detection In Aerial Images	Signal, Image and Video Processing, 17, 4 (2023)
35259	0.859	Yadav A.; Fujita M.; Kumar B.	Lightweight Dl-Based Drone Image Classification For Surveillance Applications	Proceedings - International SoC Design Conference 2024, ISOCC 2024 (2024)