基于深度学习的航空监视方法研究

我国是一个幅员辽阔的国家，地理条件复杂，常规的国土安全巡检方法会耗费大量人力物力。为此，提出了一种基于深度学习的航空监视方法，其利用无人机从高空采集图像，并利用卷积神经网络对采集图像进行分类判断，从而对场景进行监视。其目的在于用人工智能的手段，通过无人机代替人工进行巡检，从而提高国土安全监视效率。为此，本文建立了包含10种不同场景的俯视视角的数据库。通过卷积神经网络模型，对不同场景的图像特征进行学习，使得模型可以分辨不同的场景。为了验证本方法的可行性，本文在10种空基视角的数据库上进行了实验，结果显示其分类准确率达到97%。说明本方法可满足安全监视的需求，为实现智能监视提供了思路。

Research on aviation monitoring method based on deep learning

Our nation has a vast territory, which holds variate geology and climate condition. In order to ensure homeland security, it is usually necessary for the relevant personnel to carry out routine inspections, which will consume a lot of manpower and resources. To this end, this paper proposes an aeronautical surveillance method based on deep learning, which uses drones to collect images from high altitude, and uses convolutional neural networks to classify and judge the collected images to monitor the scene. The purpose is to use artificial intelligence to replace the manual inspection by drones, thereby improving the efficiency of homeland security monitoring. To this end, this thesis establishes a database of top-down perspectives containing 10 different scenarios. Through the convolutional neural network model, the image features of different scenes are learned, so that the model can distinguish different scenes. In order to verify the feasibility of this method, this paper carried out experiments on 10 kinds of space-based perspective databases, and the results showed that the classification accuracy reached 97%. This shows that this method can meet the needs of security monitoring and provides ideas for intelligent monitoring.