AirTrack: Onboard Deep Learning Framework for Long-Range Aircraft Detection and Tracking

• URL: http://arxiv.org/abs/2209.12849v3
• Date: Mon, 20 Mar 2023 21:25:23 GMT
• Title: AirTrack: Onboard Deep Learning Framework for Long-Range Aircraft Detection and Tracking
• Authors: Sourish Ghosh and Jay Patrikar and Brady Moon and Milad Moghassem Hamidi and Sebastian Scherer
• Abstract summary: AirTrack is a real-time vision-only detect and tracking framework that respects the size, weight, and power constraints of sUAS systems. We show that AirTrack outperforms state-of-the art baselines on the Amazon Airborne Object Tracking (AOT) Empirical evaluations show that our system has a probability of track of more than 95% up to a range of 700m.
• Score: 3.3773749296727535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detect-and-Avoid (DAA) capabilities are critical for safe operations of unmanned aircraft systems (UAS). This paper introduces, AirTrack, a real-time vision-only detect and tracking framework that respects the size, weight, and power (SWaP) constraints of sUAS systems. Given the low Signal-to-Noise ratios (SNR) of far away aircraft, we propose using full resolution images in a deep learning framework that aligns successive images to remove ego-motion. The aligned images are then used downstream in cascaded primary and secondary classifiers to improve detection and tracking performance on multiple metrics. We show that AirTrack outperforms state-of-the art baselines on the Amazon Airborne Object Tracking (AOT) Dataset. Multiple real world flight tests with a Cessna 182 interacting with general aviation traffic and additional near-collision flight tests with a Bell helicopter flying towards a UAS in a controlled setting showcase that the proposed approach satisfies the newly introduced ASTM F3442/F3442M standard for DAA. Empirical evaluations show that our system has a probability of track of more than 95% up to a range of 700m. Video available at https://youtu.be/H3lL_Wjxjpw .

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