Abstract
In the past ten years, civil drone technology has developed rapidly, and UaV (Unmanned aerial Vehicle) has been widely used in various industries. Especially in the field of aerial remote sensing, the emergence of UaV technology has enabled the geographical information of remote areas that are not concerned to be quickly presented. However, UaV aerial photography is greatly affected by the weather. Pictures that use aerial drones for aerial photography in rainy weather will appear noise. In this paper, how to eliminate the noise of aerial image is to be talked, the multi-channel pruning technology is used to pruning the RnResNet network. Based on this, a new anti-convergence-convolution neural network noise reduction system for the operation of UaV airborne embedded equipment is proposed. The system is used to eliminate noise in the aerial image. This type of noise reducer has got rid of the current situation that the neural network noise reducer consumes too much power and is inefficient, and has certain advantages.
| Original language | English |
|---|---|
| Article number | 8959217 |
| Pages (from-to) | 15144-15158 |
| Number of pages | 15 |
| Journal | IEEE Access |
| Volume | 8 |
| DOIs | |
| State | Published - 2020 |
| Externally published | Yes |
Keywords
- ResNet
- SlimRGBD
- UaV
- channel pruning
- generative adversarial networks
- image noise reduction
- sparse training