Augmented Reality for Real Object Detection
Wei-Xuan You, Chih-Sheng Huang, Kai-Ming Hu, Tzu-Hsin Liu, Kuan-Ting Lai · 2023
In the era of AI booming, object detection is an essential technology for computer vision tasks and widely adopted in autonomous driving. We propose a method to enhance object detection accuracy by adding virtual objects to real scenes through augmented reality, thereby quickly generating a large amount of data to facilitate model training. In addition, Augmented Reality (AR) can create data for rare scenarios in real worlds, such as a car flipping over on the road or a cargo overturned, which can alleviate the long-tail problem of AI models. Furthermore, our tool can generate both 2D and 3D bounding boxes directly. To verify our method, we performed transfer learning on YOLOv7 pre-trained model using 30,766 AR synthesized images of 4 traffic-related classes: Person, Car, Bicycle and Motorcycle. The new detector was evaluated on the COCO dataset. Experiments showed that our method can increase the detector accuracy as well its ability of detecting small objects.