论文标题
Airtrack：船上深度学习框架，用于远程飞机检测和跟踪
AirTrack: Onboard Deep Learning Framework for Long-Range Aircraft Detection and Tracking
论文作者
论文摘要
检测和避免（DAA）功能对于无人飞机系统（UAS）的安全操作至关重要。本文介绍了Airtrack，这是一个仅实时视觉检测和跟踪框架，尊重SUAS系统的大小，重量和功率（交换）约束。鉴于遥远飞机的低信噪比（SNR），我们建议在深度学习框架中使用完整的分辨率图像，以使连续的图像对齐以消除自我动态。然后，对齐的图像将在级联的初级和次级分类器中下游使用，以改善多个指标的检测和跟踪性能。我们表明，Airtrack在亚马逊机载对象跟踪（AOT）数据集上胜过最先进的基线。多次现实世界飞行测试与塞斯纳（Cessna）182与通用航空交通相互作用，并进行了其他近碰撞飞行测试，贝尔直升机在受控设置中飞向UAS，展示了拟议的方法可满足新引入的ASTM F3442/F3442M标准DAA标准。经验评估表明，我们的系统的概率超过95％，范围超过700m。视频可在https://youtu.be/h3ll_wjxjpw上找到。
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 .
