Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark.
2021
Unmanned Aerial Vehicles (UAV) can pose a major risk
for aviation safety, due to both negligent and malicious
use. For this reason, the automated detection and tracking of UAV is a fundamental task in aerial security systems.
Common technologies for UAV detection include visibleband and thermal infrared imaging, radio frequency and
radar. Recent advances in deep neural networks (DNNs)
for image-based object detection open the possibility to
use visual information for this detection and tracking task.
Furthermore, these detection architectures can be implemented as backbones for visual tracking systems, thereby
enabling persistent tracking of UAV incursions. To date,
no comprehensive performance benchmark exists that applies DNNs to visible-band imagery for UAV detection and
tracking. To this end, three datasets with varied environmental conditions for UAV detection and tracking, comprising a total of 241 videos (331,486 images), are assessed
using four detection architectures and three tracking frameworks. The best performing detector architecture obtains
an mAP of 98.6% and the best performing tracking framework obtains a MOTA of 98.7%. Cross-modality evaluation is carried out between visible and infrared spectrums,
achieving a maximal 82.8% mAP on visible images when
training in the infrared modality. These results provide the
first public multi-approach benchmark for state-of-the-art
deep learning-based methods and give insight into which
detection and tracking architectures are effective in the UAV
domain.
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