Real Time Semantic Segmentation

Real-time semantic segmentation aims to rapidly and accurately classify each pixel in an image, a crucial task for applications like autonomous driving and robotics. Current research emphasizes developing lightweight and efficient models, often employing architectures that combine convolutional neural networks (CNNs) and transformers, or that utilize innovative feature refinement and fusion techniques to improve accuracy while maintaining high frame rates. This focus on efficiency is driven by the need to deploy these models on resource-constrained hardware like embedded systems and mobile devices, impacting fields ranging from environmental monitoring to medical image analysis. The ongoing development of efficient and accurate real-time semantic segmentation models is significantly advancing the capabilities of numerous applications that require immediate scene understanding.