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首页> 外文会议>International Congress on Human-Computer Interaction, Optimization and Robotic Applications >Deep Learning Based Object Detection and Recognition of Unmanned Aerial Vehicles
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Deep Learning Based Object Detection and Recognition of Unmanned Aerial Vehicles

机译：基于深度学习的物体检测与识别无人航空公司

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摘要

In this study, the methods of deep learning-based detection and recognition of the threats, evaluated in terms of military and defense industry, by unmanned aerial vehicles (UAV) are presented. In the proposed approach, firstly, the training for machine learning on the objects is carried out using convolutional neural networks, which is one of the deep learning algorithms. By choosing the Faster-RCNN and YoloV4 architectures of the deep learning method, it is aimed to compare the achievements of the accuracy in the training process. In order to be used in the training and testing stages of the recommended methods, data sets containing images selected from different weather, land conditions and different time periods of the day are determined. The model for the detection and recognition of the threatening elements is trained, using 2595 images. The method of detecting and recognizing the objects is tested with military operation images and records taken by the UAVs. While an accuracy rate of 93% has been achieved in the Faster-RCNN architecture in object detection and recognition, this rate has been observed as 88% in the YoloV4 architecture.
机译：在本研究中，提出了基于深入的学习的检测和识别威胁，在军事和国防工业方面评估的威胁的方法，由无人驾驶航空公司（UAV）评估。在所提出的方法中，首先，使用卷积神经网络进行对象的机器学习培训，这是一个深度学习算法之一。通过选择深度学习方法的速度-RCNN和YOLOV4架构，旨在比较培训过程中准确性的成就。为了用于推荐方法的培训和测试阶段，确定包含从不同天气，土地条件和当天的不同时间段中选择的图像的数据集。使用2595个图像训练威胁元件的检测和识别模型。检测和识别对象的方法用军事操作图像和无人机拍摄的记录测试。在对象检测和识别中，在更快的RCNN架构中实现了93％的精度率，而且在Yolov4架构中，此速率已被观察到88％。

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