HeMoDU: High-Efficiency Multi-Object Detection Algorithm for Unmanned Aerial Vehicles on Urban Roads
Author:
Shi Hanyi1, Wang Ningzhi2, Xu Xinyao3, Qian Yue3, Zeng Lingbin3ORCID, Zhu Yi1
Affiliation:
1. Army Engineering University of PLA (AEU), Nanjing 210007, China 2. Anhui University (AHU), Hefei 230601, China 3. National University of Defense Technology (NUDT), Changsha 410073, China
Abstract
Unmanned aerial vehicle (UAV)-based object detection methods are widely used in traffic detection due to their high flexibility and extensive coverage. In recent years, with the increasing complexity of the urban road environment, UAV object detection algorithms based on deep learning have gradually become a research hotspot. However, how to further improve algorithmic efficiency in response to the numerous and rapidly changing road elements, and thus achieve high-speed and accurate road object detection, remains a challenging issue. Given this context, this paper proposes the high-efficiency multi-object detection algorithm for UAVs (HeMoDU). HeMoDU reconstructs a state-of-the-art, deep-learning-based object detection model and optimizes several aspects to improve computational efficiency and detection accuracy. To validate the performance of HeMoDU in urban road environments, this paper uses the public urban road datasets VisDrone2019 and UA-DETRAC for evaluation. The experimental results show that the HeMoDU model effectively improves the speed and accuracy of UAV object detection.
Funder
Youth Fund of the National Natural Science Foundation of China
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