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NBBOX: Noisy Bounding Box Improves Remote Sensing Object Detection
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DOI：10.1109/LGRS.2025.3527712.png)
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摘要
En 中文
Data augmentation has shown significant advancements in computer vision to improve model performance over the years, particularly in scenarios with limited and insufficient data. Currently, most studies focus on adjusting the image or its features to expand the size, quality, and variety of samples during training in various tasks including object detection. However, we argue that it is necessary to investigate bounding box transformations as a data augmentation technique rather than image-level transformations, especially in aerial imagery due to potentially inconsistent bounding box annotations. Hence, this letter presents a thorough investigation of bounding box transformation in terms of scaling, rotation, and translation for remote sensing object detection. We call this augmentation strategy Noise Injection into Bounding Box (NBBOX). We conduct extensive experiments on DOTA and DIOR-R, both well-known datasets that include a variety of rotated generic objects in aerial images. Experimental results show that our approach significantly improves remote sensing object detection without whistles and bells, and it is more time-efficient than other state-of-the-art augmentation strategies.
Keyword:
Object detection
Noise
Remote sensing
Training
Translation
Data augmentation
Location awareness
Feature extraction
Data models
Annotations
noisy bounding box
oriented bounding box
remote sensing object detection
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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