Self-paced cross-modality transfer learning for efficient road segmentation

Accurate road segmentation is a prerequisite for autonomous driving. Current state-of-the-art methods are mostly based on convolutional neural networks (CNNs). Nevertheless, their good performance is at expense of abundant annotated data and high computational cost. In this work, we address these two issues by a self-paced cross-modality transfer learning framework with efficient projection CNN. To be specific, with the help of stereo images, we first tackle a relevant but easier task, i.e. free-space detection with well developed unsupervised methods. Then, we transfer these useful but noisy knowledge in depth modality to single RGB modality with self-paced CNN learning. Finally, we only need to fine-tune the CNN with a few annotated images to get good performance. In addition, we propose an efficient projection CNN, which can improve the fine-grained segmentation results with little additional cost. At last, we test our method on KITTI road benchmark. Our proposed method surpasses all published methods at a speed of 15fps.