Scale Information Enhancement for Few-Shot Object Detection on Remote Sensing Images

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 rem...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 15; no. 22; p. 5372
Main Authors Yang, Zhenyu, Zhang, Yongxin, Zheng, Jv, Yu, Zhibin, Zheng, Bing
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2023
Subjects
Accuracy
Artificial intelligence
Comparative analysis
Data collection
Datasets
Deep learning
Design
Embedding
few-shot object detection
Image enhancement
Image segmentation
Machine learning
meta learning
Methods
Neural networks
Object recognition
Performance enhancement
Remote sensing
remote sensing images
scale information
Sensors
Sparsity
Technology application
Telematics
Training
China
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15225372