Estimation of Fractal Dimension and Semantic Segmentation of Motion-Blurred Images by Knowledge Distillation in Autonomous Vehicle

Abstract

Research on semantic segmentation for remote sensing road scenes advanced significantly, driven by autonomous driving technology. However, motion blur from camera or subject movements hampers segmentation performance. To address this issue, we propose a knowledge distillation-based semantic segmentation network (KDS-Net) that is robust to motion blur, eliminating the need for image restoration networks. KDS-Net leverages innovative knowledge distillation techniques and edge-enhanced segmentation loss to refine edge regions and improve segmentation precision across various receptive fields. To enhance the interpretability of segmentation quality under motion blur, we incorporate fractal dimension estimation to quantify the geometric complexity of class-specific regions, allowing for a structural assessment of predictions generated by the proposed knowledge distillation framework for autonomous driving. Experiments on well-known motion-blurred remote sensing road scene datasets (CamVid and KITTI) demonstrate mean IoU scores of 72.42% and 59.29%, respectively, surpassing state-of-the-art methods. Additionally, the lightweight KDS-Net (21.44 M parameters) enables real-time edge computing, mitigating data privacy concerns and communication overheads in internet of vehicles scenarios.