Is Mask R-CNN a good fit for what I want to do?

1. How accurate is mask R-CNN?
2. What is mask R-CNN used for?
3. What is the mask in Mask R-CNN?
4. What is mask R-CNN in deep learning?

How accurate is mask R-CNN?

The pre-processed and annotated images are used to train and validate the Mask R-CNN Classifier. Our experimental results show that damage can be classified efficiently with 95.13% accuracy on a custom dataset and 96.87% on randomly picked images.

What is mask R-CNN used for?

Mask R-CNN uses anchor boxes to detect multiple objects, objects of different scales, and overlapping objects in an image. This improves the speed and efficiency for object detection. Anchor boxes are a set of predefined bounding boxes of a certain height and width.

What is the mask in Mask R-CNN?

Mask R-CNN is an extension of Faster R-CNN and works by adding a branch for predicting an object mask (Region of Interest) in parallel with the existing branch for bounding box recognition.

What is mask R-CNN in deep learning?

Mask R-CNN is a popular deep learning instance segmentation technique that performs pixel-level segmentation on detected objects [1]. The Mask R-CNN algorithm can accommodate multiple classes and overlapping objects. You can create a pretrained Mask R-CNN network using the maskrcnn object.