Name
Abstract | ||
|---|---|---|
The number of space debris increases greatly in the last decades due to the intense outer space exploration, making a deteriorating earth orbit. The detecting, dodging and removing of space debris become a remarkable international issue. Among them, the detection of extremely dim target is still an open question. In this paper, we propose a novel dim target extraction method in single-frame star image based on convolutional neural network. The network is designed to extract the features of different spatial scales, the feature maps are up-sampled by deconvolution, and the multi-layer feature maps are fused to achieve the pixel-level classification. Experiments show that the method proposed outperforms the state-of-the-art especially on the dim target detection. |
Year | Venue | Field |
|---|---|---|
2018 | BigMM | Space debris,Computer vision,Convolutional neural network,Computer science,Deconvolution,Image based,Outer space,Artificial intelligence,Earth's orbit |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danna Xue | 1 | 0 | 1.01 |
| Yushu Zheng | 2 | 0 | 0.68 |
| Jinqiu Sun | 3 | 33 | 8.27 |
| Yu Zhu | 4 | 88 | 12.65 |
| Yaoqi Hu | 5 | 5 | 1.46 |
| Yanning Zhang | 6 | 1613 | 176.32 |