Instance Segmentation

What is R-CNN?

R-CNN, which stands for Region-based Convolutional Neural Network, is a type of deep learning model used for object detection in computer vision tasks. The term “R-CNN” actually refers to a family of models that share a common approach to object detection. The key idea behind R-CNN is to divide the object detection task into two stages: region proposal and object classification....

What is Fast R-CNN?

Fast R-CNN is an improved version of R-CNN, which aim to improve the efficiency and speed of the original model with the following additional steps:...

Instance Segmentation

This segmentation identifies each instance (occurrence of each object present in the image and colors them with different pixels). It basically works to classify each pixel location and generate the segmentation mask for each of the objects in the image. This approach gives more idea about the objects in the image because it preserves the safety of those objects while recognizing them....

What is Mask R-CNN?

Mask R-CNN (Mask Region-based Convolutional Neural Network) is an extension of the Faster R-CNN architecture that adds a branch for predicting segmentation masks on top of the existing object detection capabilities. It was introduced to address the task of instance segmentation, where the goal is not only to detect objects in an image but also to precisely segment the pixels corresponding to each object instance....

Conclusion

In conclusion, Mask R-CNN’s ability to simultaneously detect and segment objects with high accuracy positions it as a powerful tool for various applications, from human pose estimation to autonomous vehicles....

Mask R-CNN – FAQs

Q. What is mask R-CNN used for?...