Deep-learning-based moving target detection for unmanned air vehicles
| Autor: | Qingtao Yu, Fenghua He, Haodi Yao, Xiaowei Xing, Jie Ma |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Engineering business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Kalman filter Statistical classification 020901 industrial engineering & automation Position (vector) 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Computer vision Information acquisition Artificial intelligence Resource consumption business Monocular camera |
| Zdroj: | 2017 36th Chinese Control Conference (CCC). |
| Popis: | In this paper, a deep learning network is investigated to detect moving targets for a UAV equipped with monocular camera. An algorithm based on fully convolutional network is proposed to obtain the position and moving direction of targets. A Kalman filter is incorporated into the proposed algorithm to increase the accuracy of target position information acquisition. The experimental results show the effectiveness of the proposed algorithm with a relatively low hardware resource consumption. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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