Automatic “Ground Truth” Annotation and Industrial Workpiece Dataset Generation for Deep Learning
文献类型：期刊论文
| 作者 | Fu-Qiang Liu; Zong-Yi Wang |
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2020 |
| 卷号 | 17期号:4页码:539-550 |
| 关键词 | Deep learning dataset generation automatic annotation neural networks industrial workpiece dataset. |
| ISSN号 | 1476-8186 |
| DOI | 10.1007/s11633-020-1221-8 |
| 英文摘要 | In industry, it is becoming common to detect and recognize industrial workpieces using deep learning methods. In this field, the lack of datasets is a big problem, and collecting and annotating datasets in this field is very labor intensive. The researchers need to perform dataset annotation if a dataset is generated by themselves. It is also one of the restrictive factors that the current method based on deep learning cannot expand well. At present, there are very few workpiece datasets for industrial fields, and the existing datasets are generated from ideal workpiece computer aided design (CAD) models, for which few actual workpiece images were collected and utilized. We propose an automatic industrial workpiece dataset generation method and an automatic ground truth annotation method. Included in our methods are three algorithms that we proposed: a point cloud based spatial plane segmentation algorithm to segment the workpieces in the real scene and to obtain the annotation information of the workpieces in the images captured in the real scene; a random multiple workpiece generation algorithm to generate abundant composition datasets with random rotation workpiece angles and positions; and a tangent vector based contour tracking and completion algorithm to get improved contour images. With our procedures, annotation information can be obtained using the algorithms proposed in this paper. Upon completion of the annotation process, a json format file is generated. Faster R-CNN (Faster R-convolutional neural network), SSD (single shot multibox detector) and YOLO (you only look once: unified, real-time object detection) are trained using the datasets proposed in this paper. The experimental results show the effectiveness and integrity of this dataset generation and annotation method. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/42277]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | College of Automation, Harbin Engineering University, No. 145 Nantong Road, Nangang District, Harbin 150001, China |
| 推荐引用方式 GB/T 7714 | Fu-Qiang Liu,Zong-Yi Wang. Automatic “Ground Truth” Annotation and Industrial Workpiece Dataset Generation for Deep Learning[J]. International Journal of Automation and Computing,2020,17(4):539-550. |
| APA | Fu-Qiang Liu,&Zong-Yi Wang.(2020).Automatic “Ground Truth” Annotation and Industrial Workpiece Dataset Generation for Deep Learning.International Journal of Automation and Computing,17(4),539-550. |
| MLA | Fu-Qiang Liu,et al."Automatic “Ground Truth” Annotation and Industrial Workpiece Dataset Generation for Deep Learning".International Journal of Automation and Computing 17.4(2020):539-550. |
入库方式： OAI收割
来源：自动化研究所
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