Knowledge Generation and Distillation for Road Segmentation in Intelligent Transportation Systems

The rapid development of generative AI has opened a new era in Intelligent Transportation Systems (ITS). However, deploying high-performance segmentation models on resource-constrained edge devices remains challenging due to their substantial computational demands. To address this problem, in this work, we propose a lightweight road segmentation framework termed Knowledge Generation and Distillation (KGD). In the KGD, a lightweight Student model learns from a high-precision Teacher model. This approach balances accuracy and computational efficiency. To further enhance the knowledge transfer process, we introduce a knowledge distillation loss to better supervise the discrepancy between the Teacher and Student models. Meanwhile, we incorporate graph convolution to capture complex spatial dependencies. This can effectively enhance the understanding of road structure and irregular boundaries. Additionally, we built a Multi-scale Lightweight Spatial Attention (MS-LSA) module to focus on multi-scale spatial road information. Experimental results demonstrate that the proposed KGD achieves 96.33% and 94.02% in Max F1-measure and average precision(AP) on KITTI-Road, with only 1.17M in parameters and scores 6.73ms/frame in inference speed. It achieves a superior balance between accuracy and efficiency compared to mainstream deep networks. These advantages make KGD suitable for real-time use in large-scale ITS applications, such as smart traffic monitoring, autonomous vehicle perception, and adaptive traffic control systems.