Instance segmentation is a challenging computer vision task that requires the prediction of object instances and their per-pixel segmentation mask. This makes it a hybrid of semantic segmentation and object detection. It detects and delineates each distinct object of interest appearing in an image. Mask RCNN model is common for instance segmentation that has several versions for improving this task. We proposed a simple comparison between Fifteenth different version frameworks from Mask-RCNN for object instance segmentation. Our survey representing the difference between the popular versions of Mask R-CNN. The Mask R-CNN method extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. The results in most versions were implemented on of the COCO dataset that created for instance segmentation tasks.
Hassan, E., El-Rashidy, N., & M. Talaa, F. (2022). Review: Mask R-CNN Models. Nile Journal of Communication and Computer Science, 3(1), 17-27. doi: 10.21608/njccs.2022.280047
MLA
Esraa Hassan; Nora El-Rashidy; fatma M. Talaa. "Review: Mask R-CNN Models". Nile Journal of Communication and Computer Science, 3, 1, 2022, 17-27. doi: 10.21608/njccs.2022.280047
HARVARD
Hassan, E., El-Rashidy, N., M. Talaa, F. (2022). 'Review: Mask R-CNN Models', Nile Journal of Communication and Computer Science, 3(1), pp. 17-27. doi: 10.21608/njccs.2022.280047
VANCOUVER
Hassan, E., El-Rashidy, N., M. Talaa, F. Review: Mask R-CNN Models. Nile Journal of Communication and Computer Science, 2022; 3(1): 17-27. doi: 10.21608/njccs.2022.280047
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