Advanced Active Learning Strategies for Object Detection

Future self-driving cars must be able to perceive and understand their surroundings. Deep learning based approaches promise to solve the perception problem but require a large amount of manually labeled training data. Active learning is a training procedure in which the model itself selects interesting samples for labeling based on their uncertainty, with substantially less data required for training. Recent research in active learning has mostly focused on the simple image classification task. In this paper, we propose novel methods to estimate sample uncertainties for 2D and 3D object detection using Ensembles. We moreover evaluate different training strategies including Continuous Training to alleviate increasing training times introduced by the active learning cycle. Finally, we investigate the effects of active learning on imbalanced datasets and possible interactions with class weighting. Experiment results show both increased time saving around 55% and data saving rates of around 30%. For the 3D object detection task, we show that our proposed uncertainty estimation method is valid, saving 35% of labeling efforts and thus is ready for application for automotive object detection use cases.