AirTrack: 機上深層学習による長距離航空機の検出と追跡フレームワーク
AirTrack: Onboard Deep Learning Framework for Long-Range Aircraft Detection and Tracking
小型無人航空機のSWaP制約下で、画像の位置合わせとカスケード分類器を組み合わせたリアルタイム視覚のみの航空機検出・追跡手法を提案し、700m先まで95%以上の追跡率を実現した。
著者: Sourish Ghosh, Jay Patrikar, Brady Moon, Milad Moghassem Hamidi, Sebastian Scherer
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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 .
