Fully Convolutional Networks for Automatically Generating Image Masks to Train Mask R-CNN

• URL: http://arxiv.org/abs/2003.01383v2
• Date: Thu, 20 May 2021 06:53:45 GMT
• Title: Fully Convolutional Networks for Automatically Generating Image Masks to Train Mask R-CNN
• Authors: Hao Wu, Jan Paul Siebert and Xiangrong Xu
• Abstract summary: 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. This paper proposes a novel automatically generating image masks method for the state-of-the-art Mask R-CNN deep learning method. 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.
• Score: 4.901462756978097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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