Performance Evaluation and Analysis of Drone-Based Vehicle Detection Techniques From Deep Learning Perspective

Bisio, Igor; Haleem, Halar; Garibotto, Chiara; Lavagetto, Fabio; Sciarrone, Andrea

doi:10.1109/jiot.2021.3128065

Cited by 18 publications

(6 citation statements)

References 36 publications

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“…It can be grassy areas without pedestrians. The drone operator must be able to secure the ground under the drone without moving persons [17][18][19][20].…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Use of Unmanned Aerial Vehicles for Traffic Surveys

Kampf

Kubina

Bartuška

et al. 2022

LOGI – Scientific Journal on Transport and Logistics

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The paper deals with the topic of monitoring traffic using unmanned aerial vehicles. The main research goal was to test the possibility of using aerial vehicles for traffic engineering applications, especially for determining the traffic intensity on roads and for evaluating the data on traffic using video footage. The practical part of the paper focuses on the evaluation of data on traffic obtained by means of unmanned aerial vehicle at a selected intersection of roads. The data are evaluated using SW based on work with neural networks. A stationary system composed of an unmanned aircraft, camera system with a stabiliser, ground source with voltage converter, wire unwinding system and accessories were created by the staff of the ITB for the purpose of video recording the traffic from a 20-meter height. Such a tethered unmanned aerial system enabled a long-term operation in the air and a video recording lasting 1 hour at least.

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“…It can be grassy areas without pedestrians. The drone operator must be able to secure the ground under the drone without moving persons [17][18][19][20].…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Use of Unmanned Aerial Vehicles for Traffic Surveys

Kampf

Kubina

Bartuška

et al. 2022

LOGI – Scientific Journal on Transport and Logistics

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show abstract

“…Lastly, the outputted map of the static environment can also be used for path planning and obstacle avoidance. Improved strategies of autonomous control can benefit industrial pick and place robots or collaborative surveillance agents [ 51 – 53 ].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A featureless approach for object detection and tracking in dynamic environments

et al. 2023

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One of the challenging problems in mobile robotics is mapping a dynamic environment for navigating robots. In order to disambiguate multiple moving obstacles, state-of-art techniques often solve some form of dynamic SLAM (Simultaneous Localization and Mapping) problem. Unfortunately, their higher computational complexity press the need for simpler and more efficient approaches suitable for real-time embedded systems. In this paper, we present a ROS-based efficient algorithm for constructing dynamic maps, which exploits the spatial-temporal locality for detecting and tracking moving objects without relying on prior knowledge of their geometrical features. A two-prong contribution of this work is as follows: first, an efficient scheme for decoding sensory data into an estimated time-varying object boundary that ultimately decides its orientation and trajectory based on the iteratively updated robot Field of View (FoV); second, lower time-complexity of updating the dynamic environment through manipulating spatial-temporal locality available in the object motion profile. Unlike existing approaches, the snapshots of the environment remain constant in the number of moving objects. We validate the efficacy of our algorithm on both V-Rep simulations and real-life experiments with a wide array of dynamic environments. We show that the algorithm accurately detects and tracks objects with a high probability as long as sensor noise is low and the speed of moving objects remains within acceptable limits.

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“…Based on the energy consumption, the research work [12] presented a novel approach to jointly optimise the 3D flight path and data collection schedule of a fixed-wing UAV for secure and energy-efficient data collection under an interception attack. The article [13], based on a mathematically oriented approach, presented a UAV object delivery scenario, where in the presence of multiple no-fly zones, a UAV starts from the departure point, collects the objects from ground users, and finally delivers the collected objects to the destination [14].…”

Section: A Related Workmentioning

confidence: 99%

UAV Trajectory Optimisation in Smart Cities Using Modified A^* Algorithm Combined With Haversine and Vincenty Formulas

Andreas

Mavromoustakis

Batalla

et al. 2023

IEEE Trans. Veh. Technol.

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Performance Evaluation and Analysis of Drone-Based Vehicle Detection Techniques From Deep Learning Perspective

Cited by 18 publications

References 36 publications

Use of Unmanned Aerial Vehicles for Traffic Surveys

Use of Unmanned Aerial Vehicles for Traffic Surveys

A featureless approach for object detection and tracking in dynamic environments

UAV Trajectory Optimisation in Smart Cities Using Modified A^* Algorithm Combined With Haversine and Vincenty Formulas

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Performance Evaluation and Analysis of Drone-Based Vehicle Detection Techniques From Deep Learning Perspective

Cited by 18 publications

References 36 publications

Use of Unmanned Aerial Vehicles for Traffic Surveys

Use of Unmanned Aerial Vehicles for Traffic Surveys

A featureless approach for object detection and tracking in dynamic environments

UAV Trajectory Optimisation in Smart Cities Using Modified A* Algorithm Combined With Haversine and Vincenty Formulas

Contact Info

Product

Resources

About

UAV Trajectory Optimisation in Smart Cities Using Modified A^* Algorithm Combined With Haversine and Vincenty Formulas