论文标题
无人机与鸟类分类的序列模型
Sequence Models for Drone vs Bird Classification
论文作者
论文摘要
由于无人机成本降低并且无人机技术有所改善，无人机检测已成为对象检测的重要任务。但是，当对比度较弱，远距离可见度较弱时，很难检测到遥远的无人机。在这项工作中，我们提出了几种序列分类体系结构，以减少无人机轨道的假阳性比率。此外，我们提出了一个新的无人机与鸟类序列分类数据集，以训练和评估拟议的架构。 3D CNN，LSTM和基于变压器的序列分类体系结构已在拟议的数据集上进行了培训，以显示提出的想法的有效性。如实验所示，使用序列信息，鸟类分类和总体F1分别可以分别提高73％和35％。在所有序列分类模型中，基于R（2+1）D的完全卷积模型可产生最佳的转移学习和微调结果。
Drone detection has become an essential task in object detection as drone costs have decreased and drone technology has improved. It is, however, difficult to detect distant drones when there is weak contrast, long range, and low visibility. In this work, we propose several sequence classification architectures to reduce the detected false-positive ratio of drone tracks. Moreover, we propose a new drone vs. bird sequence classification dataset to train and evaluate the proposed architectures. 3D CNN, LSTM, and Transformer based sequence classification architectures have been trained on the proposed dataset to show the effectiveness of the proposed idea. As experiments show, using sequence information, bird classification and overall F1 scores can be increased by up to 73% and 35%, respectively. Among all sequence classification models, R(2+1)D-based fully convolutional model yields the best transfer learning and fine-tuning results.
