Improvement of YOLOv3 Algorithm in Workpiece Detection
文献类型：会议论文
| 作者 | Li, Xiang3; Wang JT(王金涛)2,3; Xu F(徐方)1,2,3; Song JL(宋吉来)1,2 |
| 出版日期 | 2019 |
| 会议日期 | July 29 - August 2, 2019 |
| 会议地点 | Suzhou, China |
| 关键词 | object detection YOLOv3 deep learning ISODATA |
| 页码 | 1063-1068 |
| 英文摘要 | In order to solve the problem of high time complexity and low generalization of traditional methods in the human-machine collaboration scene, this paper applies the YOLOv3 deep learning network to the part of workpiece recognition and detection of the robot workpiece capture. According to the specific application scenarios, the corresponding data set is created to train the YOLOv3 model, and the anchor value suitable for the data set is obtained by the iterative self-organizing data analysis(ISODATA) clustering algorithm. A systematic and comprehensive data augmentation of the data set is carried out for the case where the self-made data set is small and the scene is single. Considering that the target to he detected is small and the background of the detection scene is simple, the YOLOv3 basic network architecture is appropriately pruned. Combining the shallow features with the deep features makes the detection time reduced 4ms while the accuracy of the model is basically unchanged. The comparison experiment on the self-made dataset shows that the improved YOLOv3 algorithm has a mean average precision(mAP) of 0.990 and an average detection time of 60ms. Compared with the original YOLOv3 algorithm, the accuracy of the improved YOLOv3 algorithm is improved by 6%, and the average detection time is reduced by 8ms. |
| 产权排序 | 2 |
| 会议录 | Proceedings of 9th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISSN号 | 2379-7711 |
| ISBN号 | 978-1-7281-0770-7 |
| WOS记录号 | WOS:000569550300184 |
| 源URL | [http://ir.sia.cn/handle/173321/27668]  |
| 专题 | 沈阳自动化研究所_其他 |
| 通讯作者 | Li, Xiang |
| 作者单位 | 1.State Key Laboratory of Robots, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.Shenyang SIASUN Robot & Automation Co., Ltd., Shenyang 110168, China 3.Northeastern University, Shenyang 110819, China |
| 推荐引用方式 GB/T 7714 | Li, Xiang,Wang JT,Xu F,et al. Improvement of YOLOv3 Algorithm in Workpiece Detection[C]. 见:. Suzhou, China. July 29 - August 2, 2019. |
入库方式： OAI收割
来源：沈阳自动化研究所
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