论文标题
无人机管理：使用深神经网络进行空中监视的无监督异常检测
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance
论文作者
论文摘要
异常检测是自主监视系统的关键目标，应该能够提醒异常观察。在本文中，我们使用深层神经网络提出了一个整体异常检测系统，以监视关键基础设施（例如机场，港口，仓库）使用无人机（UAV）。首先，我们提出了一种启发式方法，用于明确表示鸟类视图中对象的空间布局。然后，我们提出了一个深层神经网络架构，用于无监督的异常检测（UAV-ADNET），该检测经过了共同的环境表示和GPS标签的培训。与文献中的研究不同，我们结合了GPS和图像数据以预测异常观察结果。我们在空中监视数据集中对几个基线评估了模型，并表明它在场景重建和几个异常检测任务中的性能更好。代码，训练的模型，数据集和视频将在https://bozcani.github.io/uavadnet上找到。
Anomaly detection is a key goal of autonomous surveillance systems that should be able to alert unusual observations. In this paper, we propose a holistic anomaly detection system using deep neural networks for surveillance of critical infrastructures (e.g., airports, harbors, warehouses) using an unmanned aerial vehicle (UAV). First, we present a heuristic method for the explicit representation of spatial layouts of objects in bird-view images. Then, we propose a deep neural network architecture for unsupervised anomaly detection (UAV-AdNet), which is trained on environment representations and GPS labels of bird-view images jointly. Unlike studies in the literature, we combine GPS and image data to predict abnormal observations. We evaluate our model against several baselines on our aerial surveillance dataset and show that it performs better in scene reconstruction and several anomaly detection tasks. The codes, trained models, dataset, and video will be available at https://bozcani.github.io/uavadnet.
