What is semantic segmentation?

Segmentation is the process of separating a picture into smaller parts. One of the most important jobs in computer vision is object identification, and segmentation provides the foundation for this. It’s a crucial component of artificial intelligence systems for many real-world applications, including autonomous vehicles, medical picture analysis, and more.

  • Semantic Segmentation is the process of identifying a certain kind of picture and then isolating that type from other types of images using a mask.

When numerous items of the same class or type are grouped together, we say that we are doing semantic segmentation. To illustrate the pixel boundaries of all the people or automobiles in a picture is one use of semantic segmentation. Instance segmentation, on the other hand, attempts to single out each distinct instance of a class.
There are three phases to semantic segmentation:

  • Identifying and labeling a certain visual feature.
  • Finding the thing and putting a box around it to localize it.
  • Segmentation is the process of clustering similar pixels in a targeted area of an image using a mask.

Present-day picture segmentation methods rely on deep learning architectures.

Semantic segmentation models

In order to divide up an image’s pixels into distinct groups, researchers have turned to a specific sort of deep learning technique called a semantic segmentation model. These models are trained on massive volumes of labeled visual data. A semantic segmentation model’s output is an image that has been categorized pixel by pixel.

Examples of well-known models for semantic segmentation include:

  • U-Net is a CNN built specifically for image segmentation. U-design Net incorporates two paths: one in which the picture is downsampled, and another in which it is upsampled. Convolutional and max-pooling layers make up the shrinking route, while convolutional and up-sampling layers make up the expanding one. The model is trained on a huge collection of labeled photos, and the result is an image that has been segmented such that each pixel has a corresponding class label.Medical imaging activities, such as segmenting cells and nuclei in microscope pictures, rely heavily on U-Net because of its capacity to handle tiny objects and fine features; nevertheless, U-Net is also utilized in other applications, such as satellite imagery analysis and self-driving automobiles.
  • Mask R-CNN borrows its architecture from the Faster R-CNN model, which employs a region proposal network (RPN) to propose a series of candidate regions. Mask R-CNN is an expansion of Faster R-CNN that predicts object masks in addition to bounding boxes. A fully convolutional network (FCN) called the mask branch uses the feature maps produced by the last convolutional layer of the backbone network to produce a mask specific to each item.As opposed to previous object recognition and instance segmentation models, Mask R-CNN excels in producing high-quality object masks that may be utilized in several contexts, including but not limited to image editing, video tracking, and autonomous vehicle navigation.
  • DeepLab employs a method called atrous convolution, also known as dilated convolution, which is based on the convolutional neural network (CNN) architecture. Consequently, the model may pick up on broad scene information in the picture without sacrificing resolution.Image segmentation, object identification, and captioning are just some of the typical uses for DeepLab. It excels in situations where substantial background information is necessary, such as autonomous vehicles and the segmentation of street-view photos.

Applications of Semantic Segmentation

  • Medical imaging

    Segmenting medical pictures, such as those from a CT or MRI scan, using image semantic segmentation allows for the identification and classification of various kinds of tissue. This has applications in neurology and cancer for both diagnosis and therapy planning.

  • Security

    For security and monitoring reasons, it may be used to recognize and follow certain objects in surveillance videos.

  • Autonomous vehicles

    Semantic classes may be used to detect and categorize humans, vehicles, and road signs for use by autonomous vehicles. Autonomous cars may make better judgments and increase their safety using this data.

  • Robotics

    In robotics, object detection and localization using semantic segmentation may be used to direct robot movement and enhance their capacity for interaction with their surroundings.

  • Augmented reality and virtual reality

    Things in real-world photos may be identified and segmented using image semantic segmentation for use in augmented reality applications, where virtual objects are superimposed on top of the actual environment.

  • Satellite and aerial imagery

    Large-scale aerial and satellite photos may be analyzed and classified with the help of semantic segmentation. Useful for managing cities, natural resources, and emergencies.