ICNet

ICNet (Image Cascade Network) is a high-performance deep learning architecture designed for real-time semantic segmentation of high-resolution images (up to 1024x2048). As we move into 2026, ICNet remains a critical benchmark and production choice for resource-constrained environments like autonomous vehicle compute units and industrial edge devices. The architecture utilizes a unique cascade feature fusion unit and cascade label guidance to achieve an optimal balance between speed and accuracy. Unlike standard segmentation models that process high-resolution inputs through heavy backbones, ICNet employs a multi-branch architecture: a low-resolution branch for semantic consistency, a medium-resolution branch for detail, and a high-resolution branch for boundary refinement. This hierarchical approach allows for inference speeds exceeding 30 FPS on high-end 2026 hardware while maintaining high Mean Intersection over Union (mIoU) scores. Its technical longevity is sustained by its integration into major frameworks like PaddleSeg and PyTorch, making it a staple for engineers building low-latency perception stacks for robotics, urban planning, and real-time surveillance systems.