Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks
Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks
复制标题
利用卷积神经网络实现可见光和热成像无人机监控
DOI：
10.1017/atsip.2018.30
复制
发表时间：
2018
影响因子：
3.2
通讯作者：
C.
中科院分区：
文献类型：
--
作者：
Yeon;Yueru Chen;Jongmoo Choi;C.
本文报道了一种集成了基于深度学习的检测和跟踪模块的可见和热无人机监控系统。采用深度学习方法进行无人机检测的最大挑战是缺乏训练无人机图像，特别是热无人机图像。为了解决这个问题，我们开发了两种数据增强技术。一种是基于模型的无人机增强技术，该技术可以自动生成带有无人机位置边界框标签的可见无人机图像。另一个是利用对抗数据增强方法来创建热无人机图像。为了跟踪小型无人机，我们利用连续图像帧之间的残差信息。最后，我们提出了一个集成的检测和跟踪系统，其性能优于仅包含检测或跟踪的每个单独模块。实验表明，即使在合成数据上进行训练，该系统在具有复杂背景的真实无人机图像上也表现良好。USC无人机检测和跟踪数据集与用户标记的边界框对公众开放。
This paper reports a visible and thermal drone monitoring system that integrates deep-learning-based detection and tracking modules. The biggest challenge in adopting deep learning methods for drone detection is the paucity of training drone images especially thermal drone images. To address this issue, we develop two data augmentation techniques. One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location. The other is exploiting an adversarial data augmentation methodology to create thermal drone images. To track a small flying drone, we utilize the residual information between consecutive image frames. Finally, we present an integrated detection and tracking system that outperforms the performance of each individual module containing detection or tracking only. The experiments show that, even being trained on synthetic data, the proposed system performs well on real-world drone images with complex background. The USC drone detection and tracking dataset with user labeled bounding boxes is available to the public.