Introduction to Mask R-CNN

In recent years, there has been significant progress in the field of object identification and semantic segmentation. The Fast/Faster R-CNN and Fully Convolutional Network (FCN) frameworks have played a crucial role in these advancements, providing flexibility, resilience, and fast training and inference times.

However, instance segmentation remains a challenging task, requiring accurate recognition of all objects in an image as well as precise segmentation of each instance. This combines the tasks of object detection, where the goal is to classify and localize individual objects using bounding boxes, and semantic segmentation, where the goal is to classify each pixel into a fixed set of categories without distinguishing object instances.

Despite the complexity of this task, the Mask R-CNN approach has proven to be a simple, versatile, and efficient solution that can outperform previous state-of-the-art instance segmentation results. In this post, we will delve into the details of Mask R-CNN, its operation, applications, and more.

What is Mask R-CNN?

Mask R-CNN is a deep neural network designed for solving segmentation problems in machine learning and computer vision. It can identify and classify different objects in an image or video, and return bounding boxes, classes, and masks for each object.

Mask R-CNN is an extension of the region-based convolutional neural network Faster R-CNN and is considered the most advanced CNN for image and instance segmentation.

Mask R-CNN can perform two types of image segmentation: semantic segmentation and instance segmentation.

Semantic segmentation is a process of classifying each pixel in an image into a predetermined set of categories, without distinguishing between different object instances. In other words, it involves identifying and classifying related items at the pixel level.

Instance segmentation, also known as instance recognition, involves accurately identifying every object in an image and finely segmenting each instance. It combines elements of object localization, object detection, and object categorization, and is able to clearly distinguish each object that belongs to the same category.

The Working of Mask R-CNN

The Mask R-CNN algorithm has two stages of operation. In the first stage, it suggests regions in the input image where an object may be present. In the second stage, it uses this information to predict the class of the object, refine the bounding box, and create a pixel-level mask for the object.

Both stages of the Mask R-CNN algorithm rely on a backbone structure, which is a pre-trained convolutional neural network that provides a feature extraction service for the rest of the algorithm.


Backbone is a deep neural network that is structured like a Feature Pyramid Network (FPN). It consists of lateral connections, top-down pathways, and bottom-up pathways. The bottom-up pathway is typically a ConvNet, such as ResNet or VGG, which extracts features from raw images. The top-down pathway creates a feature pyramid map that is similar in size to the bottom-up pathway.

Lateral connections are created between comparable levels of the two pathways through convolution and addition operations. The FPN is able to outperform other single ConvNets because it preserves strong semantic characteristics at different resolution scales.

Stage One

In first stage, a lightweight neural network called the Region Proposal Network (RPN) scans the entire top-down pathway (also known as the feature map) of the FPN to identify potential areas that contain objects. However, to connect the position of the features to the raw image, we use anchors. Anchors are collections of boxes with predetermined sizes and positions within the image.

Based on certain Intersection over Union (IoU) values, some anchors are assigned ground-truth classes (either object or background) and bounding boxes. The RPN uses anchors of various scales to determine the location of the object on the feature map and the dimensions of its bounding box.

Because convolution, downsampling, and upsampling preserve features in the same relative positions as the objects in the original image, they do not move the objects.

Stage Two

In the second stage, a different neural network examines the areas identified by the previous stage and creates object classes (multi-categorical categorization), bounding boxes, and masks. This process has a similar structure to the RPN.

The main difference between the two stages is that the second stage uses the ROIAlign technique to find the relevant feature map regions without the use of anchors. It also includes a branch that generates pixel-level masks for each object.

After the output of the RoIAlign layer is passed to the Mask head, which consists of two convolution layers, it produces a mask for each region of interest (RoI), segmenting the image pixel by pixel.

Significant Features of Mask RCNN

Mask R-CNN is an improvement on Faster R-CNN by adding a branch for object mask prediction in addition to the existing branch for bounding box recognition. Mask R-CNN is able to run at 5 frames per second and adds only a small overhead to Faster R-CNN.

It is also easy to train and extend to other tasks, such as estimating human poses within the same framework. In general, Mask R-CNN performs better than any single-model approach on all objectives.

Applications of Mask R-CNN

Mask R-CNN is useful for a variety of computer vision applications because it can generate segmented masks. Some potential applications include:

  • Human pose estimation
  • Motion capture
  • Autonomous vehicles
  • Surveillance systems
  • Drone image mapping
  • Generating maps using satellite imagery

Advantages of Mask R-CNN

There are several advantages to using Mask R-CNN:

  • It is easier to train and outperforms Faster R-CNN on many tasks.
  • It consistently performs better than any other single-model approach on all challenges.
  • While it is highly effective, it only slightly increases the overhead of Faster R-CNN.
  • It is flexible and can be easily adapted to different tasks, such as estimating human posture within the same framework.

Issues with Mask R-CNN

There are some limitations to Mask R-CNN:

  • It can only process static images, so it is unable to analyze temporal information about the subject, such as dynamic hand movements.
  • It may struggle to recognize objects with low-resolution motion blur.

Final Thoughts

Traditional object detectors like YOLO, SSDs, and Faster R-CNNs can only identify the bounding box coordinates of an object in an image, but they cannot provide information about the shape of the object itself.

Mask R-CNN, on the other hand, is able to generate pixel-wise masks for each object in an image, allowing us to separate the foreground object from the background. Additionally, Mask R-CNN can handle more complex objects and shapes better than other computer vision algorithms.

Ashwin Joy

I'm the face behind Pythonista Planet. I learned my first programming language back in 2015. Ever since then, I've been learning programming and immersing myself in technology. On this site, I share everything that I've learned about computer programming.

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