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首页> 外文会议>International conference on graphic and image processing >Efficient Airport Detection Using Region-based Fully Convolutional Neural Networks
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Efficient Airport Detection Using Region-based Fully Convolutional Neural Networks

机译：使用基于区域的全卷积神经网络进行有效的机场检测

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摘要

This paper presents a model for airport detection using region- based fully convolutional neural networks. To achieve fast detection with high accuracy, we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units (GPUs) to speed up the training and testing time. For lack of labeled data, we transferred the convolutional layers of ZF net pretrained by ImageNet to initialize the shared convolutional layers, then we retrained the model using the alternating optimization training strategy. The proposed model has been tested on an airport dataset consisting of 600 images. Experiments show that the proposed method can distinguish airports in our dataset from similar background scenes almost real-time with high accuracy, which is much better than traditional methods.
机译：本文提出了一种使用基于区域的全卷积神经网络进行机场检测的模型。为了实现高精度的快速检测，我们在区域提议过程和机场检测过程之间共享了conv层，并使用了图形处理单元（GPU）来加快培训和测试时间。由于缺少标记数据，我们转移了由ImageNet预训练的ZF网络的卷积层以初始化共享的卷积层，然后使用交替优化训练策略对模型进行了重新训练。所提出的模型已在包含600张图像的机场数据集上进行了测试。实验表明，该方法能够以较高的精度实时地将我们的数据集中的机场与相似的背景场景区分开，这比传统方法要好得多。

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