4.5 Article

Multirole UAVs Supported Parking Surveillance System

Journal

MOBILE NETWORKS & APPLICATIONS
Volume -, Issue -, Pages -

Publisher

SPRINGER
DOI: 10.1007/s11036-022-02045-y

Keywords

Unmanned Aerial Vehicles (UAVs); Traveling salesman problem (TSP); Vehicle Routing Problem (VRP); Object detection; Convolutional Neural Networks; Tensorflow

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This paper discusses the possible application of UAVs in recognizing improperly parked vehicles. It uses fleets of drones with different roles to address this issue. Through a case study and the use of algorithms, drone trajectories are determined while accuracy is improved through the use of synthesized training sets and object detection algorithms. Additionally, the paper describes the architecture of the Smart UAV Monitoring System and tests its functionality in real conditions.
The paper deals with the possible application of Unmanned Aerial Vehicles (UAVs) in recognizing irregular traffic situations. This primarily refers to improperly parked vehicles. Fleets of drones are used, and their roles can be observers or inspectors, where each of the roles has specially defined tasks. A case study was conducted in a real urban environment. Traveling salesman problem and Vehicle Routing Problem algorithms were used to determine drone trajectories. The study assumes difficulties in collecting a sufficient number of images for successful training of Convolutional Neural Network, so a synthesized training set was used. This process assumes the use of object detection algorithms. Accuracy was tested on a small test set of real-life images. It has been shown that by moving some of the real-life images from the test set to the training set, the accuracy can be increased. In addition, the paper describes the architecture of the Smart UAV Monitoring System for Parking Supervision (SmartUAV-PS), and tests its functioning in real conditions.

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