Weak Target Detection in High-Resolution Remote Sensing Images by Combining Super-Resolution and Deformable FPN
| Autor: | Zhenqiang Qin, Yanfeng Gu, Shujia Ye, Tongyuan Zou, Guoming Gao, Yang Bai |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Pixel
Computer science Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Superresolution Object detection Convolution Image (mathematics) Feature (computer vision) Pyramid 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pyramid (image processing) Image resolution Remote sensing |
| Zdroj: | IGARSS |
| DOI: | 10.1109/igarss39084.2020.9323260 |
| Popis: | Weak target detection plays an important role in military and civilian fields. However, due to the limitation of the target size and the influence of complex background, the detection of weak target is a huge challenge. Therefore, based on high-resolution remote sensing image, this paper proposes a weak target detection network which combines super-resolution and deformable convolution. Firstly, the high-resolution remote sensing image is expanded and enhanced to eliminate the influence of complex background. Secondly, a detection network based on the deformable convolution and feature pyramid network (FPN) is used to solve the problem of less information caused by the fewer target pixels. In addition, this paper establishes a detection dataset only containing weak vehicles. The experimental results show that the proposed method achieves better detection results in the weak target detection problem. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|