论文标题
基于极化参数构建网络的极化视觉任务的端到端CNN框架
An end-to-end CNN framework for polarimetric vision tasks based on polarization-parameter-constructing network
论文作者
论文摘要
极化图像之间的像素操作对于处理极化信息很重要。对于缺乏此类操作，极化信息不能在卷积神经网络（CNN）中得到充分利用。在本文中，提出了一个新颖的端到端CNN框架，用于极化视觉任务，这使网络能够充分利用极化图像。该框架由两个子网络组成：一个极化参数构建网络（PPCN）和一个任务网络。 PPCN用1x1卷积内核在CNN形式的图像之间实现像素的操作。它将原始的极化图像作为输入，并将极化 - 参数图像输出到任务网络，以便完成Vison任务。通过一起培训，PPCN可以学会为任务网络和数据集提供最合适的极化参数图像。以更快的R-CNN为任务网络，实验结果表明，与现有方法相比，所提出的框架在对象检测任务中实现了更高的平均平均精液（MAP）
Pixel-wise operations between polarimetric images are important for processing polarization information. For the lack of such operations, the polarization information cannot be fully utilized in convolutional neural network(CNN). In this paper, a novel end-to-end CNN framework for polarization vision tasks is proposed, which enables the networks to take full advantage of polarimetric images. The framework consists of two sub-networks: a polarization-parameter-constructing network (PPCN) and a task network. PPCN implements pixel-wise operations between images in the CNN form with 1x1 convolution kernels. It takes raw polarimetric images as input, and outputs polarization-parametric images to task network so as to complete a vison task. By training together, the PPCN can learn to provide the most suitable polarization-parametric images for the task network and the dataset. Taking faster R-CNN as task network, the experimental results show that compared with existing methods, the proposed framework achieves much higher mean-average-precision (mAP) in object detection task
