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Deep neural network for precision multi-band infrared image segmentation

机译：精密多频段红外图像分割深神经网络

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Image segmentation is one of the fundamental steps in computer vision. Separating targets from background clutter with high precision is a challenging operation for both humans and computers. Currently, segmenting objects from IR images is done by tedious manual work. The implementation of a Deep Neural Network (DNN) to perform precision segmentation of multi-band IR video images is presented. A customized pix2pix DNN with multiple layers of generative encoder/decoder and discriminator architecture is used in the IR image segmentation process. Real and synthetic images and ground truths are employed to train the DNN. Iterative training is performed to achieve optimum accuracy of segmentation using a minimal number of training data. Special training images are created to enhance the missing features and to increase the segmentation accuracy of the objects. Retraining strategies are developed to minimize the DNN training time. Single pixel accuracy has been achieved in IR target boundary segmentation using DNNs. The segmentation accuracy between the customized pix2pix DNN and simple thresholding, GraphCut, simple neural network and ResNet models are compared.
机译：图像分割是计算机视觉的基本步骤之一。从高精度背景杂波分离的目标是人类和电脑一个具有挑战性的操作。目前，从红外图像分割的对象是通过繁琐的手动完成工作。深神经网络（DNN）的执行来执行多频带IR视频图像的精度分割被呈现。甲定制pix2pix DNN与多层生成编码器/解码器和鉴别器架构在IR图像分割处理中使用。真实的和合成的图像和基础事实被用来训练DNN。执行迭代训练用的训练数据的最小数量，以实现分割的最佳精度。特别训练图像的设置是为了增强缺少的功能，并增加了物体的分割精度。再培训策略的开发，以减少DNN的训练时间。单像素精度已经IR对象边界分割使用DNNs已经实现。定制pix2pix DNN和简单的阈值，GraphCut，简单的神经网络和RESNET模型进行了比较之间的分割精度。

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