Name
Title | ||
|---|---|---|
Manipulator Grabbing Position Detection With Information Fusion Of Color Image And Depth Image Using Deep Learning |
Abstract | ||
|---|---|---|
In order to ensure stable gripping performance of manipulator in a dynamic environment, a target object grab setting model based on the candidate region suggestion network is established with the multi-target object and the anchor frame generation measurement strategy overcoming external environmental interference factors such as mutual interference between objects and changes in illumination. In which, the success rate of model detection is improved by adding small-scale anchor values for small area grabbing target position detection. Further, 94.3% crawl detection success rate is achieved on the multi-target detection data sets using the information fusion of color image and depth image. The methods in this paper effectively improve the model's robustness and crawl success rate. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s12652-020-02843-w | JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING |
Keywords | DocType | Volume |
Manipulator, Grabbing position detection, Information fusion, Deep learning | Journal | 12 |
Issue | ISSN | Citations |
12 | 1868-5137 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Du Jiang | 1 | 0 | 1.01 |
| Gongfa Li | 2 | 2 | 1.77 |
| Ying Sun | 3 | 0 | 1.01 |
| Jiabing Hu | 4 | 0 | 0.34 |
| Juntong Yun | 5 | 4 | 4.44 |
| Ying Liu | 6 | 0 | 0.34 |