FastUAV-NET: A Multi-UAV Detection Algorithm for Embedded Platforms

Electronics

Huseyin Kusetogullari¹  Amir Yavariabdi²  Hasan Cicek²  Turgay Celik³ 

[1] Department of Computer Science, Blekinge Institute of Technology, 371 41 Karlskrona, Sweden;Mechatronics Engineering Department, Faculty of Engineering, KTO Karatay University, 42020 Konya, Turkey;School of Electrical and Information Engineering and the Wits Institute of Data Science, University of the Witwatersrand, Johannesburg 2000, South Africa;

关键词: deep learning;    CNN;    detection and tracking;    Unmanned Aerial Vehicle;    UAVs pursuit-evasion;   

DOI  :  10.3390/electronics10060724

来源: DOAJ

【 摘 要 】

In this paper, a real-time deep learning-based framework for detecting and tracking Unmanned Aerial Vehicles (UAVs) in video streams captured by a fixed-wing UAV is proposed. The proposed framework consists of two steps, namely intra-frame multi-UAV detection and the inter-frame multi-UAV tracking. In the detection step, a new multi-scale UAV detection Convolutional Neural Network (CNN) architecture based on a shallow version of You Only Look Once version 3 (YOLOv3-tiny) widened by Inception blocks is designed to extract local and global features from input video streams. Here, the widened multi-UAV detection network architecture is termed as FastUAV-NET and aims to improve UAV detection accuracy while preserving computing time of one-step deep detection algorithms in the context of UAV-UAV tracking. To detect UAVs, the FastUAV-NET architecture uses five inception units and adopts a feature pyramid network to detect UAVs. To obtain a high frame rate, the proposed method is applied to every $n^{th}$ frame and then the detected UAVs are tracked in intermediate frames using scalable Kernel Correlation Filter algorithm. The results on the generated UAV-UAV dataset illustrate that the proposed framework obtains 0.7916 average precision with 29 FPS performance on Jetson-TX2. The results imply that the widening of CNN network is a much more effective way than increasing the depth of CNN and leading to a good trade-off between accurate detection and real-time performance. The FastUAV-NET model will be publicly available to the research community to further advance multi-UAV-UAV detection algorithms.

【 授权许可】

Unknown