Few-Shot Object Detection in Remote-Sensing Images via Label-Consistent Classifier and Gradual Regression

With the abomination of time-consuming or even impractical large-scale labeling, few-shot object detection (FSOD) based on natural scenes has attracted extensive attention. However, directly migrating FSOD methods designed for natural images to large-size remote-sensing images (RSIs) still remains a...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-14
Main Authors: Liu, Yanxing, Pan, Zongxu, Yang, Jianwei, Zhang, Bingchen, Zhou, Guangyao, Hu, Yuxin, Ye, Qixiang
Format: Article
Language:English
Subjects:
Aspect ratio
Datasets
Degradation
Detection
Detectors
Feature extraction
Few-shot learning
Labeling
Labels
Object detection
Object recognition
Performance degradation
Proposals
Recall
Remote sensing
remote-sensing images (RSIs)
Sensors
Shape
Shape recognition
Source code
Training
transfer learning
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Description
Summary:With the abomination of time-consuming or even impractical large-scale labeling, few-shot object detection (FSOD) based on natural scenes has attracted extensive attention. However, directly migrating FSOD methods designed for natural images to large-size remote-sensing images (RSIs) still remains a challenge. 1) Labels of novel instances within the base dataset are inconsistently assigned between the base training and the few-shot fine-tuning stage, which confuses the detector and leads to significant performance degradation over novel classes. 2) The region proposal network (RPN) of detectors cannot provide sufficient high-quality proposals for remote-sensing objects with various aspect ratios and irregular shapes, leading to decreased detection performance. To tackle these issues, we specify a novel few-shot object detector for RSIs, to avoid the significant performance degradation caused by inconsistent labeling assignments, as well as efficiently leveraging the novel instances that existed in the base dataset. Furthermore, the proposed detector utilizes a coarse-to-fine regression method with an enhanced feature extractor called Gradual RPN to improve the recall of the RPN. Experiments on a newly constructed few-shot detection benchmark show that our approach improves the mAP of novel classes by up to 8.4% and the average recall of the RPN by up to 12.3%. The source code is available at https://github.com/YanxingLiu/SAE-FSDet .
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3369666