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 .

翻译：探测与规避（DAA）能力对于无人机系统（UAS）的安全运行至关重要。本文提出AirTrack——一种实时纯视觉检测与跟踪框架，其设计兼顾小型无人机系统（sUAS）在尺寸、重量与功耗（SWaP）方面的约束。针对远程飞行器信噪比（SNR）较低的问题，我们提出在全分辨率图像上应用深度学习框架，通过连续图像配准消除自运动。对齐后的图像随后被输入级联的主分类器与辅助分类器，以在多项指标上提升检测与跟踪性能。实验表明，AirTrack在亚马逊空中目标跟踪（AOT）数据集上优于现有最优基线方法。通过与通用航空交通交互的塞斯纳182飞机多次实飞测试，以及在受控环境下贝尔直升机朝向UAS飞行的近碰撞场景测试，验证了所提方法满足新发布的ASTM F3442/F3442M DAA标准。经验评估显示，本系统在700米范围内跟踪概率超过95%。视频演示见https://youtu.be/H3lL_Wjxjpw。