How is the output of segmentObjects, SCORES, calculated?

Hi, everyone.

Sorry for my poor English.

I am a beginner in object detection research and I am trying to detect objects using Mask R-CNN.

How is the output of segmentObjects, SCORES, calculated? And what evaluation metrics are used?

Any articles, papers, etc. that you have written about this?

Answer 1

[Shubh]

Hi,

Detecting objects using Mask R-CNN is a significant step in computer vision, especially in the field of object detection and segmentation. Mask R-CNN is an extension of Faster R-CNN, a popular method for object detection, and it adds an additional branch for predicting segmentation masks on each Region of Interest (RoI), in parallel with the existing branch for classification and bounding box regression.Understanding segmentObjects and SCORES:

1. segmentObjects: This is typically a function or a part of the Mask R-CNN model that performs the actual segmentation of objects. It takes an image as input and outputs the segmented objects along with their classes and bounding boxes.
2. SCORES: In the context of Mask R-CNN, SCORES usually refers to the confidence scores of each detected object. These scores represent the probability that a detected object belongs to a particular class. The model computes these scores during the classification step for each RoI.

For object detection and segmentation, several evaluation metrics are commonly used:

1. Precision and Recall: Precision measures the accuracy of the detections (the percentage of detections that are correct), while recall measures the model's ability to detect all relevant instances.
2. mAP (mean Average Precision): This is a popular metric in object detection. It calculates the average precision values for recall levels across all classes.
3. IoU (Intersection over Union): This metric is used to measure the accuracy of an object detector on a particular dataset. It's calculated as the area of overlap between the predicted bounding box and the ground truth bounding box, divided by the area of union between these two boxes.
4. Segmentation Metrics: For the segmentation part, metrics like Pixel Accuracy, Mean IoU, and Dice Coefficient are used.

To deepen your understanding, here are some key resources and papers:

1. Original Mask R-CNN Paper:

• "Mask R-CNN" by Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick.
• Link: https://arxiv.org/abs/1703.06870

2. Faster R-CNN Paper (Foundation for Mask R-CNN):

• "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun.
• Link: https://arxiv.org/abs/1506.01497

3. MATLAB Tutorials and Implementations:

• MATLAB provides a Computer Vision Toolbox that includes functionalities for object detection and recognition. You can find tutorials and examples directly on the MATLAB website or in the documentation.
• For a specific tutorial on using Deep Learning for Object Detection in MATLAB, you can start here: https://www.mathworks.com/help/vision/ug/object-detection-using-deep-learning.html
• You can refer to MATLAB's 'maskrcnn' function here: https://www.mathworks.com/help/vision/ref/maskrcnn.html

Hope this helps!

[Tatsuya Onishi]

Hi, Shubh. Thanks for answering!

I had overlooked the paper in the official documentation.

Thanks to your help I think I will be able to solve it.