Efficient Training Method for Point Cloud-Based Object Detection Models by Combining Environmental Transitions and Active Learning

Abstract

The perceptive systems used in automated driving need to function accurately and reliably in a variety of traffic environments. These systems generally perform object detection to identify the positions and attributes of potential obstacles. Among the methods which have been proposed, object detection using three-dimensional (3D) point cloud data obtained using LiDAR has attracted much attention. However, when attempting to create a detection model, annotation must be performed on a huge amount of data. Furthermore, the accuracy of 3D object detection models is dependent on the data domains used for training, such as geographic or traffic environments, so it is necessary to train models for each domain, which requires large amounts of training data for each domain. Therefore, the objective of this study is to develop a 3D object detector for new domains, even when trained with relatively small amounts of annotated data from new domains. We propose using a model that has been trained with a large amount of labeled data for pre-trained model, and simultaneously using transfer learning with limited amount of highly effective training data, selected from the target domain by active learning. Experimental evaluations show that 3D object detection models created using the proposed method perform well at a new location. We also confirm that active learning is particularly effective only limited training data available.