Automatic “Ground Truth” Annotation and Industrial Workpiece Dataset Generation for Deep Learning

doi:10.1007/s11633-020-1221-8

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	Automatic “Ground Truth” Annotation and Industrial Workpiece Dataset Generation for Deep Learning
	Fu-Qiang Liu; Zong-Yi Wang
发表期刊	International Journal of Automation and Computing
ISSN	1476-8186
	2020
卷号	17 期号:4 页码:539-550
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.
关键词	Deep learning dataset generation automatic annotation neural networks industrial workpiece dataset.
DOI	10.1007/s11633-020-1221-8
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42277
专题	学术期刊_Machine Intelligence Research
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.

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