What Is Semantic Segmentation In Computer Vision?

Semantic segmentation is a computer vision task that assigns labels to each pixel in an image, allowing machines to recognize objects and their meanings with precision. Unlike bounding box identification, which provides a general region of an object, semantic segmentation offers detailed understanding by partitioning an image into segments and assigning semantic labels to each. The task, often termed dense prediction, involves predicting labels for each pixel, enabling computers to recognize and understand objects' context within an image. Various architectures have been developed for this purpose, including Fully Convolutional Networks (FCNs), U-Net, DeepLab, and Pyramid Scene Parsing Network (PSPNet), each utilizing different techniques to maintain spatial information and handle high-resolution data efficiently. FCNs focus on maintaining spatial information through skip connections, U-Net employs a U-shaped architecture for capturing local and global context, DeepLab uses atrous convolution for efficient pixel-level predictions, and PSPNet leverages pyramid pooling modules for multi-scale context capture. These techniques are widely applied in fields such as autonomous vehicles and medical imaging due to their ability to handle complex segmentation tasks.