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An Object Detection Algorithm for UAV Reconnaissance Image Based on Deep Convolution Network

机译：基于深卷积网络的UAV侦察图像对象检测算法

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摘要

In recent years, the UAV technology has developed rapidly and played an important role in many fields, especially in intelligence, reconnaissance, and monitoring. Object detection can provide accurate target location and target category for reconnaissance missions, providing detailed command information for commanders. However, the current object detection algorithm based on deep convolution network does not work well on detection for small objects and so cannot be applied to small objects in the reconnaissance image of UAV. In this paper, an object detection algorithm for UAV reconnaissance image based on deep convolution network is proposed. The image is adaptively divided according to the UAV flight parameters and the payload parameters before sent into the network. Through this way, small objects can be located and classified in a high accuracy of location and classification. This method can detect objects with small size, multiple quantities, and multiple categories on UAV
机译：近年来，UAV技术发展迅速，在许多领域发挥了重要作用，特别是智力，侦察和监测。对象检测可以为侦察任务提供准确的目标位置和目标类别，为指挥官提供详细的命令信息。然而，基于深度卷积网络的当前对象检测算法在对小物体的检测中不起作用，因此不能应用于UAV的侦察图像中的小对象。本文提出了一种基于深卷积网络的UAV侦察图像的对象检测算法。通过在发送到网络之前，根据UAV飞行参数和有效载荷参数自适应地划分图像。通过这种方式，可以以高精度和分类定位和分类小物体。此方法可以检测具有小尺寸，多数量和UAV的多个类别的对象

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