HEVC rate control for unmanned aerial vehicle airborne reconnaissance

Chun-lei, 赵春蕾 Zhao; Ming, 戴明 Dai; Chuan-li, 尹传历 Yin

doi:10.3788/ope.20152312.3500

光学精密工程

2015

DOI: 10.3788/ope.20152312.3500

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HEVC rate control for unmanned aerial vehicle airborne reconnaissance

赵春蕾 Zhao Chun-lei¹,

戴明 Dai Ming²,

尹传历 Yin Chuan-li³

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2021

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Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

Liu

2021

IFS

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The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

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Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

Liu

2021

IFS

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HEVC rate control for unmanned aerial vehicle airborne reconnaissance

Cited by 1 publication

References 0 publications

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

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