Enhancing object detection for remote sensing with dynamic heads in Oriented R-CNN

Hongmei Wang; Jiahe Zhang

doi:10.1117/12.3029658

21 June 2024 Enhancing object detection for remote sensing with dynamic heads in Oriented R-CNN

Hongmei Wang, Jiahe Zhang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131670N (2024) https://doi.org/10.1117/12.3029658
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

This paper aims to enhance the Oriented R-CNN framework for object detection in aerial images. Leveraging a diverse collection of high-resolution aerial imagery, including buildings, vehicles, ships, and aircraft, we propose improvements to address challenges like object scale, orientation, and occlusion common in such images. While the Oriented R-CNN method has shown promise in handling oriented objects, challenges in scale and spatial awareness remain. We introduce a novel approach that enhances the Oriented R-CNN framework by integrating dynamic heads, combining scale and spatial awareness. Through scale-aware attention mechanisms, we address issues with object scale, and spatial-aware mechanisms capture fine-grained spatial information. Extensive experiments, including the Dota dataset, validate the method's superior performance. Our contribution enhances object detection precision in remote sensing, overcoming previous limitations.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongmei Wang and Jiahe Zhang "Enhancing object detection for remote sensing with dynamic heads in Oriented R-CNN", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131670N (21 June 2024); https://doi.org/10.1117/12.3029658

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KEYWORDS

Object detection

Remote sensing

Target detection

Convolution

Data modeling

Feature extraction

Modeling

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