Abstract
Unmanned aerial vehicles are becoming common and have many productive uses. However, their increased prevalence raises safety concerns -- how can we protect restricted airspace? Knowing the type of unmanned aerial vehicle can go a long way in determining any potential risks it carries. For instance, fixed-wing craft can carry more weight over longer distances, thus potentially posing a more significant threat. This paper presents a machine learning model for classifying unmanned aerial vehicles as quadrotor, hexarotor, or fixed-wing. Our approach effectively applies a Long-Short Term Memory (LSTM) neural network for the purpose of time series classification. We performed experiments to test the effects of changing the timestamp sampling method and addressing the imbalance in the class distribution. Through these experiments, we identified the top-performing sampling and class imbalance fixing methods. Averaging the macro f-scores across 10 folds of data, we found that the majority quadrotor class was predicted well (98.16%), and, despite an extreme class imbalance, the model could also predicted a majority of fixed-wing flights correctly (73.15%). Hexarotor instances were often misclassified as quadrotors due to the similarity of multirotors in general (42.15%). However, results remained relatively stable across certain methods, which prompted us to analyze and report on their tradeoffs. The supplemental material for this paper, including the code and data for running all the experiments and generating the results tables, is available at this https URL.
Abstract (translated)
无人机变得越来越普遍，并且有很多有益的应用。然而，它们日益增加的普及也引发了对安全的担忧——我们该如何保护受限制的飞行空间？知道无人机的类型在很大程度上可以决定其潜在风险。例如，固定翼无人机可以承载更重的负载，因此可能构成更大的威胁。本文提出了一种机器学习模型，用于将无人机分类为四旋翼、六旋翼或固定翼。我们的方法有效地应用了长短时记忆（LSTM）神经网络来进行时间序列分类。我们进行了实验来测试改变时间戳采样方法和解决分类分布不均衡的效果。通过这些实验，我们确定了最有效的采样和分类不均衡解决方法。在数据集的10个fold上平均计算得到宏观f分数，我们发现，大多数四旋翼分类预测准确率很高（98.16%），尽管存在极大的分类不均衡，但模型还可以预测大多数固定翼飞行的多数（73.15%）。六旋翼实例常常被错误地分类为四旋翼，因为多旋翼通常具有相似性（42.15%）。然而，不同的方法得出的结果相对稳定，这促使我们分析和报告它们的权衡。本文的补充材料，包括所有实验的代码和数据，可在此链接下载：https://url.cn/xyz6hua
URL
https://arxiv.org/abs/2403.00565
