Name
Abstract | ||
|---|---|---|
The onboard multi-modality sensors significantly expand perception ability of Unmanned Surface Vehicle (USV). This paper aims to fully utilize various onboard sensors and enhance USV's object detection performance. We solve several unique challenges for application of USV multi-modality sensor system in the complex maritime environment. By utilizing deep learning networks, we achieved accurate object detection on water surface. We firstly propose a multi-modality sensor calibration method. The network fuses RGB images with multiple point clouds from various sensors. The well-calibrated image and point cloud are input to our deep object detection network, and conduct 3D detection through proposal generation network and object detection network. Meanwhile, we made a series of improvements to the system framework, which accelerate the detection procedures. We collected two datasets from the real-world offshore field and the simulation scenes respectively. The experiments on both datasets showed valid calibration results. On this basis, our object detection network achieves better accuracy than other methods. The performance of the proposed multi-modality sensor system meets the application requirement of our prototype USV platform. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11063-019-09998-4 | NEURAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Multi-modality sensor,Unmanned Surface Vehicle,Object detection,Sensor calibration | Journal | 52.0 |
Issue | ISSN | Citations |
SP2.0 | 1370-4621 | 0 |
PageRank | References | Authors |
0.34 | 17 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Liu | 1 | 212 | 59.74 |
| Jie Nie | 2 | 0 | 0.34 |
| Yingjian Liu | 3 | 3 | 2.82 |
| Yingying Wu | 4 | 0 | 0.34 |
| Hanxing Wang | 5 | 0 | 0.34 |
| Fangchao Qu | 6 | 0 | 0.34 |
| Wei Liu | 7 | 1 | 0.70 |
| Yangyang Li | 8 | 0 | 0.34 |