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Robust detection algorithm with triple constraints for cooperative target based on spectral residual

机译：基于谱残差的三重约束协同目标鲁棒检测算法

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摘要

The accurate detection of cooperative targets plays a key and foundational role in unmanned aerial vehicle (UAV) landing autonomously. The standard method based on fixed threshold is too susceptible to both illumination variations and interference. To overcome issues above, a robust detection algorithm with triple constraints for cooperative targets based on spectral residual (TCSR) is proposed. Firstly, by designing an asymmetric cooperative target, which comprises red background, green H and triangle target, the captured original image is converted into a Lab color space, whose saliency map is yielded by constructing the spectral residual. Then, the triple constraints are developed according to the prior knowledge of the cooperative target. Finally, the salient region in saliency map is considered as the cooperative target, and it meets the triple constraints. Experimental results in complex environments show that the proposed TCSR outperforms the standard methods in higher detection accuracy and lower false alarm rate.
机译：协作目标的准确检测在无人机自动降落中起着关键的基础作用。基于固定阈值的标准方法太容易受到光照变化和干扰的影响。为了克服上述问题，提出了一种基于频谱残差（TCSR）的具有三重约束的协作目标检测算法。首先，通过设计一个包括红色背景，绿色H和三角形目标的非对称合作目标，将捕获的原始图像转换为Lab色彩空间，并通过构造光谱残差产生其显着性图。然后，根据合作目标的先验知识来开发三重约束。最终，显着图中的显着区域被视为合作目标，并且满足三重约束。在复杂环境中的实验结果表明，所提出的TCSR在更高的检测精度和更低的误报率方面优于标准方法。

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