バーチャル世界で走る：合成データは実世界タスクの人手アノテーションを代替できるか
Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?
シミュレーションエンジンで生成した写実的な合成画像とアノテーションのみで学習した車両検出モデルが、KITTIデータセット上で人手アノテーション学習モデルを上回る性能を示した。
著者: Matthew Johnson-Roberson, Charles Barto, Rounak Mehta, Sharath Nittur Sridhar, Karl Rosaen, Ram Vasudevan
分類: cs.CV, cs.RO
原文アブストラクト
Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have relied upon massive amounts of human annotated training data. This time consuming process has begun impeding the progress of these deep learning efforts. This paper describes a method to incorporate photo-realistic computer images from a simulation engine to rapidly generate annotated data that can be used for the training of machine learning algorithms. We demonstrate that a state of the art architecture, which is trained only using these synthetic annotations, performs better than the identical architecture trained on human annotated real-world data, when tested on the KITTI data set for vehicle detection. By training machine learning algorithms on a rich virtual world, real objects in real scenes can be learned and classified using synthetic data. This approach offers the possibility of accelerating deep learning's application to sensor-based classification problems like those that appear in self-driving cars. The source code and data to train and validate the networks described in this paper are made available for researchers.
