论文标题
使用标准化 - 跨相关层在红外图像上进行小目标检测的滤波器设计
Filter design for small target detection on infrared imagery using normalized-cross-correlation layer
论文作者
论文摘要
在本文中，我们介绍了一种机器学习方法，以解决红外小目标检测滤波器设计的问题。为此，建议与神经网络的卷积层相似，我们提出了我们用于设计目标检测/识别滤波器库的标准化 - 交叉相关（NCC）层。通过在神经网络结构中采用NCC层，我们引入了一个框架，其中使用监督训练来计算最佳的滤波器形状以及在红外图像上特定目标检测/识别任务所需的最佳过滤器数量。我们还提出了均值散热NCC（MAD-NCC）层，该层是对拟议的NCC层的有效实现，特别专为FPGA系统而设计，在该系统中，避免了平方根操作以实时计算。作为一个案例研究，我们在中波红外图像上进行了昏暗的目标检测，并获得可以将昏暗目标与各种类型的背景混乱区分开的过滤器，这是我们操作概念的特定的。
In this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similarly to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduce a framework, in which supervised training is used to calculate the optimal filter shape and the optimum number of filters required for a specific target detection/recognition task on infrared images. We also propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient implementation of the proposed NCC layer, designed especially for FPGA systems, in which square root operations are avoided for real-time computation. As a case study we work on dim-target detection on mid-wave infrared imagery and obtain the filters that can discriminate a dim target from various types of background clutter, specific to our operational concept.
