Name
Abstract | ||
|---|---|---|
The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN). First, the dataset of the Marine Traffic Management Net is described and CNN's object sensing based on the Detectron2 platform is discussed. There will also be a description of the experiment and performance. In addition, this study has been conducted based on actual drone delivery operations-the first air delivery service by drones in Korea. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/s22103824 | SENSORS |
Keywords | DocType | Volume |
convolutional neural network (CNN), recognize ship structures, mask R-CNN, faster R-CNN | Journal | 22 |
Issue | ISSN | Citations |
10 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae-Jun Lim | 1 | 0 | 0.34 |
| Dae-Won Kim | 2 | 0 | 0.34 |
| Woon-Hee Hong | 3 | 0 | 0.34 |
| M. Kim | 4 | 122 | 13.25 |
| Dong-Hoon Lee | 5 | 0 | 0.34 |
| Sun-Young Kim | 6 | 0 | 0.34 |
| Jae-Hoon Jeong | 7 | 0 | 0.68 |