Efficient Active Learning Strategies for Monocular 3D Object Detection

Processing camera information to perceive their 3D surrounding is essential for building scalable autonomous driving vehicles. For this task, deep learning networks provide effective real-time solutions. However, to compensate for missing depth information in cameras compared to LiDARs, a large amount of labeled data is required for training. Active learning is a training framework where the network actively participates in the data selection process to improve data efficiency and performance. In this work, we propose an active learning pipeline for 3D object detection from monocular images. The main components of our approach are (1) two training-efficient uncertainty estimation strategies, (2) a diversity-based selection strategy to select images that contain the most diverse set of objects, (3) a novel active learning strategy more suitable for training autonomous driving perception networks. Experiments show that combining our proposed uncertainty estimation methods provides a better data saving rate and reaches a higher final performance than baselines. Furthermore, we empirically show performance gains of the presented diversity-based selection strategy and the efficiency of the proposed active learning strategy.