CSN: A compact semantic segmentation network for visual scene perception in assistive navigation

Accuracy and efficiency are essential in assistive navigation algorithms to ensure accessibility and reliability for visually impaired individuals. However, existing deep learning models often require substantial computational resources to achieve high accuracy, making them impractical for deployment on mobile devices. To address this problem, we introduce CSN, a compact semantic segmentation network designed for assistive navigation, optimizing performance in resource-constrained environments. With CSN, we introduce two innovative modules, the cascaded atrous multi-scale enhancement (CAME) layer and the dual-path residual bottleneck (DPRB) block. The CAME layer efficiently enhances multi-scale representation through feature resampling, while the DPRB block improves feature refinement with minimal computational cost. These modules enable CSN to achieve robust and reliable segmentation across diverse and complex pedestrian environments. The proposed approach achieves the best result on the challenging TrueSight dataset, demonstrating superior prediction accuracy and computational efficiency compared to state-of-the-art lightweight models. CSN achieves a mean intersection over union of 60.99%, while maintaining a low computational cost of 83.46 giga floating-point operations, a compact model size of 8.41 million parameters, and a real-time inference speed of 52.59 frames per second.