If you have ever taken a look at the parking spaces in your city or town, you will notice that there seem to be quite a lot of them. And if you don't have one available, then it is getting more difficult for you to take a taxi or bus to go to wherever your destination is. Therefore, this problem has become very important for cities around the world that are so densely populated. Mask R-CNN is widely used in image segmentation and detection tasks.
Mask R-CNN is a state-of-the-art open-source computer vision algorithm. it is widely used in different application areas including object detection tasks. R-CNN also known as Residual Image CNN and Residual Network, is an image segmentation and detection algorithm applied with a dual network architecture that exploits the information of neighboring pixels.
“Mask R-CNN” is an object detection method based on the use of a simple R-CNN architecture. This article explains Mask R-CNN implementation using python and provides you with its application and how it works.
Table of contents:
- What is Mask R-CNN?
- How does MasK R-CNN work?
- Advantages of Mask R-CNN
- Detecting parking spaces using Mask R-CNN model
- What is Mask R-CNN?
Mask R-CNN, sometimes known as Mask RCNN, is the most advanced Convolutional Neural Network (CNN) for instance, and picture segmentation. Faster R-CNN, a region-based convolutional neural network, served as the foundation for Mask R-CNN.
Knowing the idea of image segmentation is a prerequisite for understanding how Mask R-CNN operates.
Image Segmentation is a computer vision task that involves dividing a digital image into various pieces (sets of pixels, also known as image objects). In this segmentation, borders and objects are located (lines, curves, etc.).
There are 2 main types of image segmentation that fall under Mask R-CNN:
- Semantic segmentation
- Instance segmentation
In semantic segmentation, each pixel is divided into a predetermined set of categories, which does not distinguish between various object instances. In other words, semantic segmentation is concerned with identifying and classifying related objects into a single category at the pixel level.
The process of correctly identifying every object in an image while also finely segmenting each instance is known as instance segmentation, also known as instance recognition. Thus, it combines object localization, object detection, and object classification. In other words, this kind of segmentation takes a step further to clearly distinguish each object that is categorized as a comparable instance.
2) How does Mask R-CNN work?
Faster R-CNN was used to create Mask R-CNN. Faster R-CNN outputs a class label and a bounding-box offset for each candidate item, whereas Mask R-CNN adds a third branch that outputs the object mask. The additional mask output requires the extraction of a considerably more precise spatial arrangement of an object since it differs from the class and box outputs.
Faster R-expansion, CNN's Mask R-CNN, builds on its functionality by simultaneously adding a branch for predicting an object mask (Region of Interest) and the branch for bounding box detection.
3) Advantages of Mask R-CNN
- Simplicity - Mask R-CNN is simple to train
- Performance - Mask R-CNN outperforms existing, single-model entries on every task.
- Efficiency: The method is very efficient and adds only a small overhead to Faster R-CNN.
- Flexibility: Mask R-CNN is easy to generalize to other tasks. For example, it is possible to use Mask R-CNN for human pose estimation in the same framework.
4) Detecting parking spaces using the Mask R-CNN model
Now that we have seen what sorts of algorithms and models are used. Let's apply them to detect empty parking space availability. To solve this we have to break down the problem into smaller steps in a series of steps to form a pipeline.
Here’s the breakdown of steps and complete pipeline to detect empty parking spaces.
In the first step of the pipeline, find every parking spot in a frame of video. Of course, before we can determine whether parking spaces are empty, we must first identify which areas of the image are parking spaces.
The second step is to detect all the cars in each frame of the video. This will let us track the movement of each car from frame to frame. The approach here is to detect cars instead of searching for parking space. We assume that cars are already parked in a parking space.
Now, finding out which parking spots are now filled by cars and which aren't is the third stage. The outcomes of the first and second phases must be combined for this.
The final step is to notify users when a parking place becomes available. The basis for this will be changed in car positions between video frames.
We can have lots of approaches and techniques to solve each step and finally detect parking space but we use Mask R-CNN which combines the accuracy of CNNs with clever design and efficiency tricks that greatly speed up the detection process. This will run relatively fast (on a GPU) as long as we have a lot of training data to train the model.
In this article, we learned about the Mask R-CNN algorithm which is used to detect empty parking spaces. We also learned how Mask R-CNN works and what are its advantages for solving image detection tasks in real-world scenarios.