Infrared unmanned aerial vehicle detection based on generative adversarial network data augmentation

Yuan Gao; Zijuan Luo; Xuelian Yu; Kan Ren; Yunfei Ye; Qian Chen

doi:10.1117/12.2605456

24 November 2021 Infrared unmanned aerial vehicle detection based on generative adversarial network data augmentation

Yuan Gao, Zijuan Luo, Xuelian Yu, Kan Ren, Yunfei Ye, Qian Chen

Proceedings Volume 12061, AOPC 2021: Infrared Device and Infrared Technology; 120610Y (2021) https://doi.org/10.1117/12.2605456
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

As the application of UAVs in military and civilian fields becomes more and more widespread, the detection of UAVs in the low-altitude range has also become an important research direction. Compared with radar and visible light detection, infrared technology has become the major UAV detection method with its advantages of all-weather and long range. Most of the current infrared target detection methods are based on convolutional neural networks (CNN), which achieve target detection through feature extraction and feature classification. The performance of all such detection algorithms is highly dependent on their training set. A data set with a large number of samples and wide coverage tends to train a more robust and accurate detector. So, to obtain better detection effects, we perform data augmentation on the infrared UAV dataset by adversarial generative network (GAN). First, we extract the targets from the training set and train a GAN network, using its generator to obtain many new targets which are different from the training set samples, then we randomly extend these targets to the original dataset, and finally we retrain the detectors using the new dataset to achieve better detection. We created an infrared UAV image dataset for our experiments, with only a single target on each image. After data augmentation, multiple UAV targets are randomly generated. The experiments demonstrate that the new dataset trains the model with better detection results. And the GAN data augmentation can be combined with many advanced detectors to make a large improvement in detection.

Citation Download Citation

Yuan Gao, Zijuan Luo, Xuelian Yu, Kan Ren, Yunfei Ye, and Qian Chen "Infrared unmanned aerial vehicle detection based on generative adversarial network data augmentation", Proc. SPIE 12061, AOPC 2021: Infrared Device and Infrared Technology, 120610Y (24 November 2021); https://doi.org/10.1117/12.2605456

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KEYWORDS

Target detection

Unmanned aerial vehicles

Gallium nitride

Infrared radiation

Sensors

Infrared detectors

Infrared imaging

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