Active Learning for Lane Detection: A Knowledge Distillation Approach

Lane detection is a key task for autonomous driving vehicles. Currently, lane detection relies on a huge amount of annotated images, which is a heavy burden. Active learning has been proposed to reduce annotation in many computer vision tasks, but no effort has been made for lane detection. Through experiments, we find that existing active learning methods perform poorly for lane detection, and the reasons are twofold. On one hand, most methods evaluate data uncertainties based on entropy, which is undesirable in lane detection because it encourages to select images with very few lanes or even no lane at all. On the other hand, existing methods are not aware of the noise of lane annotations, which is caused by heavy occlusion and unclear lane marks. In this paper, we build a novel knowledge distillation framework and evaluate the uncertainty of images based on the knowledge learnt by the student model. We show that the proposed uncertainty metric overcomes the above two problems. To reduce data redundancy, we explore the influence sets of image samples, and propose a new diversity metric for data selection. Finally we incorporate the uncertainty and diversity metrics, and develop a greedy algorithm for data selection. The experiments show that our method achieves new state-of-the-art on the lane detection benchmarks. In addition, we extend this method to common 2D object detection and the results show that it is also effective.