DSAFNet: Dilated–Separable Convolution and Attention Fusion Network for Real-Time Semantic Segmentation
Year: 2026
Type: article
Abstract: Real-time semantic segmentation has been widely adopted in resource-constrained applications such as mobile devices, autonomous driving, and drones due to its high efficiency. However, existing lightweight networks often compromise segmentation accuracy to reduce parameter count and improve inference speed. To achieve an optimal balance among accuracy, latency, and model size, we propose the Dilated–Separable Convolution and Attention Fusion Network (DSAFNet), a lightweight real-time semantic segmentation network based on an asymmetric encoder–decoder framework. DSAFNet integrates three core components: (i) the Double-Layer Multi-Branch Depthwise Convolution (DL-MBDC) module that fuses channel splitting and multi-branch depthwise convolutions to efficiently extract multi-scale features with minimal parameters; (ii) the Multi-scale Dilated Fusion Attention (MDFA) module that utilizes factorized dilated convolutions and channel-spatial collaborative attention to expand the receptive field and reinforce key contextual features; (iii) the Multi-scale Attention Lightweight Decoder (MALD) that integrates multi-scale feature maps to generate attention-guided segmentation results. Experiments conducted on an RTX 3090 platform demonstrate that DSAFNet, with only 1.00 M parameters, achieves 74.78% mIoU and a frame rate of 74.74 FPS on the Cityscapes dataset, while 70.5% mIoU and a frame rate of 89.5 FPS on the CamVid dataset.
Source: Electronics
Authors: Wencong Lv, Xin Liu, Jianjun Zhang, Dongmei Luo, Ping Han
Institution: Wuhan University of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G4319653592
OpenAlex ID: W7130518038