Question 8What is Semantic Segmentation?1 point

Semantic Segmentation is a technique in computer vision that involves dividing an image into segments and assigning each segment to a predefined class. It's a form of pixel-level prediction because each pixel in the image is assigned a class.

Here are the steps to explain it:

1. Understanding the Concept: Semantic Segmentation is all about understanding an image at pixel level i.e., assigning each pixel in the image an object class. For example, in an outdoor image, pixels could be classified as belonging to categories like 'road', 'car', 'pedestrian', etc.

2. How it Works: Semantic Segmentation models are built on Convolutional Neural Networks (CNNs). The image is passed through a series of convolutional, pooling, and upsampling layers to produce a pixel-wise mask, which is then used to segment the image.

3. Applications: Semantic Segmentation is used in a variety of applications including autonomous driving, robotics, and photo editing software. In autonomous driving, for instance, it's used to understand the driving scene in real-time.

4. Challenges: Some of the challenges in Semantic Segmentation include differentiating between objects of the same class, handling variations in object size, and dealing with occlusions.

5. Evaluation: The performance of Semantic Segmentation models is typically evaluated using metrics like Pixel Accuracy and Mean Intersection over Union (Mean IoU).