  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
