Object Detection Based on the GrabCut Method for Automatic Mask Generation
The Mask R-CNN-based object detection method is typically very time-consuming and laborious since it involves obtaining the required target object masks during training. Therefore, in order to automatically generate the image mask, we propose a GrabCut-based automated mask generation method for obje...
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Published in | Micromachines (Basel) Vol. 13; no. 12; p. 2095 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
MDPI AG
28.11.2022
MDPI |
Subjects | |
Online Access | Get full text |
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Summary: | The Mask R-CNN-based object detection method is typically very time-consuming and laborious since it involves obtaining the required target object masks during training. Therefore, in order to automatically generate the image mask, we propose a GrabCut-based automated mask generation method for object detection. The proposed method consists of two stages. The first stage is based on GrabCut's interactive image segmentation method to generate the mask. The second stage is based on the object detection network of Mask R-CNN, which uses the mask from the previous stage together with the original input image and the associated label information for training. The Mask R-CNN model then automatically detects the relevant objects during testing. During experimentation with three objects from the Berkeley Instance Recognition Dataset, this method achieved a mean of average precision (mAP) value of over 95% for segmentation. The proposed method is simple and highly efficient in obtaining the mask of a segmented target object. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2072-666X 2072-666X |
DOI: | 10.3390/mi13122095 |