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GANsformer: A Detection Network for Aerial Images with High Performance Combining Convolutional Network and Transformer
| Content Provider | MDPI |
|---|---|
| Author | Zhang, Yan Liu, Xi Wa, Shiyun Chen, Shuyu Ma, Qin |
| Copyright Year | 2022 |
| Description | There has been substantial progress in small object detection in aerial images in recent years, due to the extensive applications and improved performances of convolutional neural networks (CNNs). Typically, traditional machine learning algorithms tend to prioritize inference speed over accuracy. Insufficient samples can cause problems for convolutional neural networks, such as instability, non-convergence, and overfitting. Additionally, detecting aerial images has inherent challenges, such as varying altitudes and illuminance situations, and blurred and dense objects, resulting in low detection accuracy. As a result, this paper adds a transformer backbone attention mechanism as a branch network, using the region-wide feature information. This paper also employs a generative model to expand the input aerial images ahead of the backbone. The respective advantages of the generative model and transformer network are incorporated. On the dataset presented in this study, the model achieves 96.77% precision, 98.83% recall, and 97.91% |
| Starting Page | 923 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14040923 |
| Journal | Remote Sensing |
| Issue Number | 4 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-14 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Object Detection Transformer Deep Learning Aerial Image Generative Model Gansformer Detection Network |
| Content Type | Text |
| Resource Type | Article |
