A deep learning system called semantic segmentation assigns a label or category to each pixel in an image. It’s used to identify a group of pixels that belong to different categories. An autonomous vehicle, for example, must be able to automobiles, pedestrians, traffic signs, pavement, and other road elements.
Many applications, such as automated driving, medical imaging, and industrial inspection, use semantic segmentation.
There aren’t just two types of semantic segmentation. You can alter the number of categories used to categorize the image’s content. This identical image could be divided into four categories, such as a person, sky, water, and background.
What is Image Segmentation?
Image segmentation is an extension of image classification in which we do localization in addition to classification. The model pinpoints where a corresponding object is present by delineating the object’s boundary, making image segmentation a superset of image classification.
What Are Real-world Use Cases of Semantic Segmentation
There are numerous uses for semantic segmentation.
It has made its way into practically all picture and video-related tasks. Image processing, 3D modeling, facial segmentation, the healthcare business, precision agriculture, and other applications use semantic segmentation.
Here are a few examples of Semantic Segmentation’s most common applications.
Scene understanding is comparable to aerial image processing, but semantic segmentation of the aerial view of the terrain is required.
Drones can spread out to examine different locations to locate people and animals in need of rescue in times of disaster, such as a flood, where this type of technology is quite valuable.
Another application of aerial image processing is in the transportation of commodities by air.
Character segmentation is effective at detecting all of the constituent pieces of many types of objects. A licence plate image is divided into parts by the algorithm. It divides a number on a plate into letters and numbers, then segments characters depending on colour, character distance, font, structure, and other factors.
Automatic licence plate recognition, also known as number plate recognition, necessitates the use of high-resolution cameras that may be adjusted to various conditions. Cameras should be firmly installed, either on a vehicle or in a fixed location, so that they can see the roads and surroundings.
What Is Semantic Segmentation: Conclusion
Here’s a quick rundown of everything we’ve talked about so far:
- Semantic segmentation is a method for distinguishing between different things in an image.
- At the pixel level, it might be regarded an image categorization problem.
- For the job of semantic segmentation, the deep learning methods we outlined have sped up the creation of algorithms that can be employed in real-world scenarios with promising outcomes.
- To produce the most accurate results, these algorithms primarily use convolutional neural networks and their modified forms.
- By decreasing the error generated during the training phase, loss functions help us to optimise the neural network.
- Semantic segmentation is used in a variety of domains, including autonomous driving, medical picture analysis, and aerial image processing.