论文标题
完全卷积的网络，用于自动生成图像面具以训练面膜R-CNN
Fully Convolutional Networks for Automatically Generating Image Masks to Train Mask R-CNN
论文作者
论文摘要
本文提出了一种新颖的自动生成图像掩模方法，以实现最先进的掩码R-CNN深度学习方法。 The Mask R-CNN method achieves the best results in object detection until now, however, it is very time-consuming and laborious to get the object Masks for training, the proposed method is composed by a two-stage design, to automatically generating image masks, the first stage implements a fully convolutional networks (FCN) based segmentation network, the second stage network, a Mask R-CNN based object detection network, which is trained on the object image FCN输出，原始输入图像和其他标签信息中的掩盖。通过实验，我们提出的方法可以自动获取图像掩模以训练蒙版R-CNN，并且它可以达到非常高的分类精度，而平均精度的90％以上的平均精度（MAP）进行分割
This paper proposes a novel automatically generating image masks method for the state-of-the-art Mask R-CNN deep learning method. The Mask R-CNN method achieves the best results in object detection until now, however, it is very time-consuming and laborious to get the object Masks for training, the proposed method is composed by a two-stage design, to automatically generating image masks, the first stage implements a fully convolutional networks (FCN) based segmentation network, the second stage network, a Mask R-CNN based object detection network, which is trained on the object image masks from FCN output, the original input image, and additional label information. Through experimentation, our proposed method can obtain the image masks automatically to train Mask R-CNN, and it can achieve very high classification accuracy with an over 90% mean of average precision (mAP) for segmentation
