@article{WANG2023, 
author = {Xiaonian WANG and Mengxuan SONG},
title = {Automatic data set generation method for instance segmentation},
year = {2023},
journal = {Experimental Technology and Management},
volume = {40},
number = {7},
pages = {28-32,40},
keywords = {3D scene reconstruction, instance segmentation, laser point cloud, data labeling},
url = {https://www.sciopen.com/article/10.16791/j.cnki.sjg.2023.07.005},
doi = {10.16791/j.cnki.sjg.2023.07.005},
abstract = {With the rapid development of autonomous driving technology in recent years, the unmanned vehicle industry has gradually become a focus of world attention. As more and more artificial intelligence algorithms are used to solve problems in environment perception, decision making and planning of unmanned vehicles, the demand for annotated data is also increasing dramatically. In order to reduce the time and labor cost of data annotation, this paper proposes an automatic generation method for image and laser data annotation based on Unity3D. Firstly, a 3D scene is constructed based on 2D map information, and then a virtual camera is used to realize the visible image and semantic segmentation data acquisition, and the annotation results of multi-line beam LIDAR are also simulated. The automatically generated annotation data in the paper can be used as a dataset for detection and segmentation tasks, as well as for image and laser fusion and complementation, thus satisfying the needs of artificial intelligence algorithms for relearning or migration learning. The proposed method in this paper is efficient and concise while ensuring data diversity and effectively solving the data annotation challenge.}
}