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Aircraft Detection using Deep Convolutional Neural Network for Small Unmanned Aircraft Systems

机译:使用深度卷积神经网络的小型无人飞机系统飞机检测

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In this paper, we propose a vision-based aircraft detection method based on a deep convolutional neural network using a single camera sensor. The proposed method detects aircraft even in complex backgrounds under the horizon and can be applied to wide range of environments. We verified our system performance using test videos consisting of a total of 17,000 frames. On the test data, our model achieved over 83% of detection rate and 0.899 precision. Our system operates at over 28 frames per second even on NVIDIA TX1 embedded board that is only 88 grams, so it is suitable for small LAS applications.
机译:在本文中,我们提出了一种基于视觉的飞机检测方法,该方法基于使用单个摄像头传感器的深度卷积神经网络。所提出的方法甚至可以在地平线下的复杂背景下检测飞机,并可以应用于广泛的环境中。我们使用包含总共17,000帧的测试视频验证了我们的系统性能。在测试数据上,我们的模型实现了83%以上的检测率和0.899的精度。即使在仅88克的NVIDIA TX1嵌入式板上,我们的系统也能以每秒28帧的速度运行,因此它适用于小型LAS应用。

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