NBBOX: Noisy Bounding Box Improves Remote Sensing Object Detection

TL;DR

This paper introduces NBBOX, a novel data augmentation technique that applies transformations to bounding boxes in aerial imagery, significantly enhancing remote sensing object detection performance.

Contribution

It investigates bounding box transformations as a data augmentation method, demonstrating its effectiveness and efficiency over existing image-level augmentation strategies.

Findings

Improves detection accuracy on DOTA and DIOR-R datasets.

More time-efficient than other augmentation methods.

Significantly enhances remote sensing object detection performance.

Abstract

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 NBBOX (Noise Injection into Bounding Box). 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.