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

翻译：本文建议为最先进的面具 R-CNN 深层学习方法采用新型的自动生成图像遮罩方法。 面具 R-CNN 方法在物体探测方面取得最佳结果,但迄今为止,将物体遮面具用于培训非常耗时费力费费费,而拟议方法则由两阶段设计组成,即自动生成图像遮罩,第一阶段实施一个完全革命网络(FCN)的分割网,第二阶段网络,基于面具 R-CNN 的物体探测网,该网接受来自FCN 输出的物体图像遮罩、原始输入图像和附加标签信息的培训。通过实验,我们拟议的方法可以自动获取图像遮罩来培训面具 R-CNN,并且可以达到非常高的分类精度,平均精度超过90%(mAP)用于分割。