DCaT: Lightweight Semantic Segmentation Model for High-Resolution Scenes

Semantic segmentation is a critical task in computer vision for analyzing and understanding scenes. However, existing segmentation models require high computational costs and memory demands, which makes them unsuitable for lightweight semantic segmentation in high-resolution scenes. To address this issue, a novel lightweight semantic segmentation model called DCaT has been proposed, specifically designed for high-resolution scenes. First, the model extracts the local low-level semantics of the image using deep separable convolution; second, the global high-level semantics of the image is obtained using a lightweight Transformer based on coordinate-aware and dynamic sparse mixed attention; then, the high-level semantics are injected into low-level semantics through the fusion module; and lastly, pixel prediction labels are outputted through the segmentation head. The experimental results of DCaT on the high- resolution dataset Cityscapes show that compared to the benchmark model, the mean intersection over union has improved by 1.5 percentage points, the model's complexity has been reduced by 26%, and the inference speed has increased by 12%. A better balance between model complexity and performance in high-resolution scenarios is achieved, thus demonstrating the effectiveness and practicality of DCaT. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.