Tiny Object Detection in High-Resolution Satellite Imagery via Oriented R-CNN with Dilated Fusion, Balanced Ranking Assignment, and Vector Mapping
Tiny Object Detection in High-Resolution Satellite Imagery
J.A.H. van Oosten (TU Delft - Aerospace Engineering)
A. Jamshidnejad – Mentor (TU Delft - Aerospace Engineering)
E.J.J. Smeur – Graduation committee member (TU Delft - Aerospace Engineering)
S. Khademi – Graduation committee member (TU Delft - Architecture and the Built Environment)
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Abstract
Tiny object detection (TOD) in satellite imagery is critical for applications including pipeline monitoring, where the detection of tiny objects, such as excavators near the pipeline networks, can prevent potential incidents. However, TOD faces challenges due to the limited pixel representation of objects, complications with Intersection over Union-based label assignment, and semantic confusion between visually similar classes. This paper introduces OE-Net, a modular extension of Oriented R-CNN,
specifically designed for TOD in satellite images. OE-Net integrates three novel components: a Dilated Fusion module to enhance the feature extraction, a Balanced Ranking Assigner to improve the anchor matching, and Vector Mapping for more precise classification of visually similar objects. The modular design of OE-Net allows its novel components to be easily integrated into other detection models. Tested on the TinyDOTA dataset, OE-Net sets a new benchmark in TOD performance, achieving a 6.3
percentage points improvement in mean Average Precision (mAP) over the Oriented R-CNN baseline. On a novel excavator detection dataset, named ExcaSat and developed for pipeline monitoring, OE-Net outperforms the Oriented R-CNN baseline by 11.1 percentage points in mAP. Furthermore, OE-Net surpasses state-of-the-art methods on both TinyDOTA and ExcaSat, establishing a new standard in computational efficiency and detection precision for satellite-based monitoring tasks. The code is available at: https://github.com/OE-jimvanoosten/mmrotate-OE.git.