Abstract
Unmanned aerial vehicles (UAVs) play an essential role in various applications, such as transportation and intelligent environmental sensing. However, due to camera motion and complex environments, it can be difficult to recognize the UAV from its surroundings thus, traditional methods often miss detection of UAVs and generate false alarms. To address these issues, we propose a novel method for detecting and tracking UAVs. First, a cross-scale feature aggregation CenterNet (CFACN) is constructed to recognize the UAVs. CFACN is a free anchor-based center point estimation method that can effectively decrease the false alarm rate, the misdetection of small targets, and computational complexity. Secondly, the region of interest-scale-crop-resize (RSCR) method is utilized to merge CFACN and region-of-interest (ROI) CFACN (ROI-CFACN) further, in order to improve the accuracy at a lower computational cost. Finally, the Kalman filter is adopted to track the UAV. The effectiveness of our method is validated using a collected UAV dataset. The experimental results demonstrate that our methods can achieve higher accuracy with lower computational cost, being superior to BiFPN, CenterNet, YoLo, and their variants on the same dataset.
Highlights
Drones, called unmanned aerial vehicles (UAVs), have become increasingly popular for both military purposes and domestic uses
When we combined the two deep learning frameworks of cross-scale feature aggregation CenterNet (CFACN) and ROI-CFACN, the accuracy and precision performance increased by 0.91% and the computational complexity was reduced
Simple online real-time tracking (SORT) tracking algorithm with the CFACN improved the CFACN detection accuracy by 1.54% mAP and computational time reduced by 4.74 ms
Summary
Called unmanned aerial vehicles (UAVs), have become increasingly popular for both military purposes and domestic uses. Due to their agility, accessibility, and low cost, drones can fly above prohibited areas and as such pose significant security risks. Remote-controlled drones have repeatedly violated the boundaries of protected areas such as airports and military bases. They may be used to engaged in illegal activities, such as invasion of privacy, smuggling, and industrial espionage [1,2]. There is a strong need to develop methods to detect drones and defend against them autonomously. Many techonolgy have been utilized to deal with this problem, including those based on acoustic [3,4], lidar [5], radar [6], RF signal detection, and optical camera [7,8,9,10,11] sensors
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