ICNet

ICNet is a deep learning framework designed for real-time semantic segmentation of high-resolution images. It builds upon the PSPNet architecture, optimizing it for efficiency without sacrificing accuracy. The core idea involves processing images at multiple resolutions and then intelligently fusing these features to produce detailed segmentation maps. The architecture uses a cascade of convolutional layers to extract features at different scales. These features are then upsampled and combined to generate a high-resolution segmentation output. This approach reduces computational complexity, enabling real-time performance on devices with limited computational resources. The models are trained on datasets like Cityscapes and evaluated based on mIoU (mean Intersection over Union) and pixel accuracy, demonstrating its practical applicability for autonomous driving, robotics, and augmented reality.