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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Infrared Target Detection in Cluttered Environments by Maximization of a Target to Clutter Ratio (TCR) Metric Using a Convolutional Neural Network
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Infrared Target Detection in Cluttered Environments by Maximization of a Target to Clutter Ratio (TCR) Metric Using a Convolutional Neural Network

机译：使用卷积神经网络最大化目标对杂波比（TCR）度量的目标来杂乱环境中的红外目标检测

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摘要

Infrared target detection is a challenging computer vision problem which involves detecting small targets in heavily cluttered conditions while maintaining a low false alarm rate. We propose a network that optimizes a "target to clutter ratio"(TCR) metric defined as the ratio of the output energies produced by the network in response to targets and clutter. A TCR-network (TCRNet) is presented in which the filters of the first convolutional layer are composed of the eigenvectors most responsive to targets or to clutter. These vectors are analytically derived via a closed form optimization of the TCR metric. The remaining convolutional layers are trained using a novel cost function also designed to optimize the TCR criterion. We evaluate the performance of the TCRNet using a public domain medium wave infrared dataset released by the US Army's Night Vision Laboratories, and compare it to the state-of-the-art detectors such as Faster regions with convolutional neural networks (R-CNN) and Yolo-v3. The TCRNet demonstrates state-of-the-art results with greater than 30% improvement in probability of detection while reducing the false alarm rate by more than a factor of two when compared to these leading methods. Experimental results are shown for both day and night time images, and ablation studies are presented which demonstrate the contribution of the first layer eigenfilters, additional convolutional layers, and the benefit of the TCR cost function.
机译：红外目标检测是一个具有挑战性的计算机视觉问题，涉及在维持低误报率的同时检测严重杂乱条件中的小目标。我们提出了一种网络，该网络优化了定义为响应于目标和杂波的网络产生的输出能量的比率的“杂波比”（TCR）度量。提出了TCR网络（TCRNET），其中第一卷积层的滤波器由最响应于目标或杂波的特征向量组成。这些载体通过TCR度量的闭合形式优化进行了分析衍生。剩余的卷积层使用新的成本函数训练，也旨在优化TCR标准。我们使用美国陆军夜视实验室发布的公共领域中波红外数据集进行了TCRNET的表现，并将其与最先进的探测器（如卷积神经网络（R-CNN）的速度更快）进行比较和yolo-v3。 TCRNET在与这些领先方法相比，检测概率的提高大于30％的最新结果，在检测概率上提高了超过30％，同时将误报报警速度超过两倍。显示实验结果对于白天和夜间图像显示，提出了烧蚀研究，其证明了第一层特征滤波器，额外的卷积层以及TCR成本函数的益处的贡献。

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