Mask r-cnn
Abstract
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code has been made available at: https://github.com/facebookresearch/Detectron.
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Mask R-CNN obtained 39.8% boxAP and 43.4% AP75 on the COCO test-dev dataset for object detection.
“obtained for object detection on the COCO test-dev dataset 39.8% boxAP and 43.4% AP75.”
From A review on modern defect detection models using DCNNs – Deep convolutional neural networks (2022) · cites this paper as [73]
Mask R-CNN can run at 5 FPS.
“It can run at 5 FPS”
From A review on modern defect detection models using DCNNs – Deep convolutional neural networks (2022) · cites this paper as [73]
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- Authors
- He, K., Gkioxari, G., Dollar, P., & Girshick, R.
- Affiliations
- Meta (United States) (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2020-02-01
- Volume
- 42
- Issue
- 2
- Pages
- 386-397
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2806070179
- Semantic Scholar
- 3a0130cfab8192a397d79fc1c7c398470157cf48
- Cited by
- 3,457
- References
- 46
- Citations by year
- 2026: 281 · 2025: 481 · 2024: 565 · 2023: 617 · 2022: 584 · 2021: 513 · 2020: 273 · 2019: 122 · 2018: 18
- FWCI
- 115.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- Mask R-CNN, instance segmentation, object detection, Faster R-CNN, human pose estimation
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