Nowadays, semantic segmentation methods for systems in road scene have a great demand. Most existing methods focus on high accuracy with low inference speed. And some approaches emphasize on speed, significantly sacrificing model accuracy. To make a trade-off between accuracy and inference speed, we propose a real-time network for semantic segmentation titled Factorized and Regular Network (FRNet), which employs an asymmetric encoder-decoder architecture with Factorized and Regular (FR) blocks. Our method achieves 70.4% mIoU on the Cityscapes test set with 1 million parameters at a speed of 127 frames per second (FPS) on a single Titan Xp at a resolution of $512\times 1024$ . We evaluate FRNet on Cityscapes, Camvid, Kitti, and Gatech datasets to identify that our network stands out from other state-of-the-art networks.
FRNet: Factorized and Regular Blocks Network for Semantic Segmentation in Road Scene
IEEE Transactions on Intelligent Transportation Systems ; 23 , 4 ; 3522-3530
2022-04-01
2306075 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Semantic video scene segmentation and transfer
British Library Online Contents | 2014
|RGB Road Scene Material Segmentation
British Library Conference Proceedings | 2023
|