Efficient Feature Extraction and Fusion for Lightweight Semantic Segmentation Networks

Semantic segmentation, pivotal in applications like autonomous driving and robotics, requires accurate pixel-wise labeling. Here we propose a novel Feature Extraction and Fusion (FEF) module, integrating dilated convolution and depth-wise separable convolution, to swiftly extract multi-scale features with enhanced accuracy and computational efficiency. Our lightweight network demonstrates a 72.6% Mean Intersection over Union (mIoU) on the Cityscapes dataset and achieves an impressive 93.7 FPS on a single GTX 1080Ti GPU, showcasing a competitive balance between speed and accuracy. This work contributes to the ongoing advancements in real-time semantic segmentation by offering a streamlined approach that maintains high performance with minimal computational overhead. The code can be found in https://figshare.com/s/ 5f3ad04a8ba0128b633f