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首页> 外文会议>4th International Symposium on Bioelectronics and Bioinformatics >Deep neural networks-based vehicle detection in satellite images
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Deep neural networks-based vehicle detection in satellite images

机译：基于深度神经网络的卫星图像车辆检测

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Vehicle detection in satellite images is an Challenging task, but meaningful at the same time. This paper propose a vehicle detection method in satellite images using Deep Convolutional Neural Network(DNN). DNN is a model of deep learning and it has a high learning capacity when dealing with images. DNN consist of several convolution layers and pooling layers, the last layer is full connection with output(this can be considered as neural network). DNN can automatically learn rich features from trainning dataset, and has achieved excellent performance in many applications such as image classification and object recognition. To benefit from this method, we propose a vehicle detection framework. Firstly we use a graph-based superpixel segmentation to extract a set of image patches, which can help us locate vehicle effectively. And then we train a DNN network to classify these pathes into vehicle and non-vehicle. Experimental results indicate that the proposed method has a good performance, with high detection rates and very few false alarms for all test road segment.
机译：卫星图像中的车辆检测是一项具有挑战性的任务，但同时也很有意义。提出了一种利用深度卷积神经网络（DNN）的卫星图像车辆检测方法。 DNN是深度学习的模型，在处理图像时具有很高的学习能力。 DNN由几个卷积层和池化层组成，最后一层与输出完全连接（可以视为神经网络）。 DNN可以从训练数据集中自动学习丰富的功能，并且在图像分类和目标识别等许多应用中均取得了出色的性能。为了从这种方法中受益，我们提出了一种车辆检测框架。首先，我们使用基于图的超像素分割来提取一组图像块，这可以帮助我们有效地定位车辆。然后，我们训练DNN网络将这些路径分类为车辆和非车辆。实验结果表明，该方法具有良好的性能，检测率高，对所有测试路段的误报率极低。

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