ERFNet

ERFNet (Efficient Residual Factorized ConvNet) is a pioneering deep learning architecture designed to provide an optimal trade-off between computational efficiency and accuracy for semantic segmentation tasks. Developed originally for autonomous driving and intelligent transportation systems, ERFNet utilizes a unique structure of 'non-bottleneck-1D' blocks. These blocks leverage factorized convolutions—decomposing a standard 3x3 kernel into sequential 3x1 and 1x3 operations—which dramatically reduces parameters and FLOPs while maintaining a wide receptive field. In the 2026 market, ERFNet continues to serve as a critical benchmark and production-grade backbone for edge deployment on low-power hardware like NVIDIA Jetson and specialized NPUs. Its architecture consists of a powerful encoder to extract features and a lightweight decoder to recover spatial resolution, achieving over 70% mIoU on the Cityscapes dataset at real-time speeds (>50 FPS on modern hardware). Its robustness and minimal memory footprint make it the preferred choice for industrial robotics, drone navigation, and real-time ADAS (Advanced Driver Assistance Systems) where latency is the primary constraint.