Scale Information Enhancement for Few-Shot Object Detection on Remote Sensing Images.
- Resource Type
- Article
- Authors
- Yang, Zhenyu; Zhang, Yongxin; Zheng, Jv; Yu, Zhibin; Zheng, Bing
- Source
- Remote Sensing. Nov2023, Vol. 15 Issue 22, p5372. 19p.
- Subject
- *REMOTE-sensing images
*REMOTE sensing
*DETECTORS
*OPTICAL remote sensing
*ACQUISITION of data
- Language
- ISSN
- 2072-4292
Recently, deep learning-based object detection techniques have arisen alongside time-consuming training and data collection challenges. Although few-shot learning techniques can boost models with few samples to lighten the training load, these approaches still need to be improved when applied to remote-sensing images. Objects in remote-sensing images are often small with an uncertain scale. An insufficient amount of samples would further aggravate this issue, leading to poor detection performance. This paper proposes a Gaussian-scale enhancement (GSE) strategy and a multi-branch patch-embedding attention aggregation (MPEAA) module for cross-scale few-shot object detection to address this issue. Our model can enrich the scale information of an object and learn better multi-scale features to improve the performance of few-shot object detectors on remote sensing images. [ABSTRACT FROM AUTHOR]