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Adaptive Switching Spatial-temporal Fusion Detection for Remote Flying Drones
IEEE Transactions on Vehicular Technology ( IF 6.8 ) Pub Date : 2020-07-01 , DOI: 10.1109/tvt.2020.2993863
Jiayang Xie , Jin Yu , Junfeng Wu , Zhiguo Shi , Jiming Chen

The drone has been applied in various areas due to its small size, high mobility and low price. However, illegal uses of drones have posed huge threats to both public safety and personal privacy. There is an urgent demand for technologies that can timely detect and counter the drones. In this paper, we propose an adaptive switching spatial-temporal fusion detection method for remote flying drones in the airspace using electrical-optical cameras, which can enhance the contrast between the target and background as well as suppressing the noises and clutters simultaneously. For each incoming video frame, a dark-attentive interframe difference method and a row-column separate black-hat method are proposed to generate temporal feature maps (TFM) and spatial feature maps (SFM), respectively, in parallel. Inspired by the phenomenon that the features in TFMs and SFMs both go strong at the regions of the intended target while they do not at other regions where noises and clutters locate, we design an adaptive switching spatial-temporal fusion mechanism to fuse the SFMs and TFMs, generating adaptive switching spatial-temporal feature maps (ASSTFM). Finally, an adaptive local threshold mechanism is used in ASSTFMs to segment the targets from backgrounds. In order to validate the effectiveness of our method, we conduct both offline experiments and field tests. The experiment results manifest that our method is superior to the other seven baseline methods and works more stably for different backgrounds and various types of drones.

中文翻译:

远程飞行无人机的自适应切换时空融合检测

无人机因其体积小、机动性强、价格低廉等特点,已被应用于各个领域。然而,无人机的非法使用对公共安全和个人隐私都构成了巨大威胁。迫切需要能够及时检测和对抗无人机的技术。在本文中,我们提出了一种使用电光相机的空域远程飞行无人机自适应切换时空融合检测方法,该方法可以增强目标与背景之间的对比度,同时抑制噪声和杂波。对于每个传入的视频帧,提出了一种暗注意帧间差异方法和一种行列分离黑帽方法来分别并行生成时间特征图 (TFM) 和空间特征图 (SFM)。受 TFM 和 SFM 中的特征在预期目标区域都很强而在噪声和杂波所在的其他区域没有的现象的启发,我们设计了一种自适应切换时空融合机制来融合 SFM 和 TFM ,生成自适应切换时空特征图(ASSTFM)。最后,在 ASSTFM 中使用自适应局部阈值机制从背景中分割目标。为了验证我们方法的有效性,我们进行了离线实验和现场测试。实验结果表明,我们的方法优于其他七种基线方法,并且对于不同背景和各种类型的无人机更稳定。
更新日期:2020-07-01
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