2021
DOI: 10.3390/rs13081416
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Toward More Robust and Real-Time Unmanned Aerial Vehicle Detection and Tracking via Cross-Scale Feature Aggregation Based on the Center Keypoint

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 t… Show more

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Cited by 6 publications
(1 citation statement)
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“…Puzanau and Nefedov (2021) studied UAV detection in a background of wind noise and designed an algorithm based on the Neyman-Pearson lemma. They found through experiments that the probability of correct UAV detection of the algorithm was 0.9 in a detection range of 200-300 m. Bao et al (2021) established a cross-scale feature aggregation centric network to identify UAV and used a Kalman filter to track the UAV. The experiment found that the method could achieve high accuracy at a lower computational cost.…”
Section: Introductionmentioning
confidence: 99%
“…Puzanau and Nefedov (2021) studied UAV detection in a background of wind noise and designed an algorithm based on the Neyman-Pearson lemma. They found through experiments that the probability of correct UAV detection of the algorithm was 0.9 in a detection range of 200-300 m. Bao et al (2021) established a cross-scale feature aggregation centric network to identify UAV and used a Kalman filter to track the UAV. The experiment found that the method could achieve high accuracy at a lower computational cost.…”
Section: Introductionmentioning
confidence: 99%