A Method of Smoke Area Segmentation for Infrared Images Based on Deep Learning
Smoke area segmentation is an efficient way to improve the performance of the infrared imaging guidance system, even though smoke is one of the main disturbances for automatic target recognition. In this paper, we proposed a smoke area segmentation method for infrared images based on deep learning....
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2019-01-01
|
| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2019.04.400 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Smoke area segmentation is an efficient way to improve the performance of the infrared imaging guidance system, even though smoke is one of the main disturbances for automatic target recognition. In this paper, we proposed a smoke area segmentation method for infrared images based on deep learning. In order to reduce the cost of data acquisition, the deep learning network for cloud segmentation is trained on our simulated image data set up by particle system and data augmentation, which substantially decreases the demand for real smoke images. Then, the Deeplab v3+ algorithm was used for smoke segmentation. When testing on the real image data set, a satisfactory segmentation result with 79 percent has been reached which can meet the usage requirements, illustrating that our method is effective and efficient. |
|---|---|
| ISSN: | 2096-5427 |