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arXiv · 2507.11040

Combining Transformers and CNNs for Efficient Object Detection in High-Resolution Satellite Imagery

Nicolas Drapier·Aladine Chetouani·Aurélien Chateigner
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Abstract

We present GLOD, a transformer-first architecture for object detection in high-resolution satellite imagery. GLOD replaces CNN backbones with a Swin Transformer for end-to-end feature extraction, combined with novel UpConvMixer blocks for robust upsampling and Fusion Blocks for multi-scale feature integration. Our approach achieves 32.95\% on xView, outperforming SOTA methods by 11.46\%. Key innovations include asymmetric fusion with CBAM attention and a multi-path head design capturing objects across scales. The architecture is optimized for satellite imagery challenges, leveraging spatial priors while maintaining computational efficiency.

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Nicolas Drapier, Aladine Chetouani, Aurélien Chateigner. 2025-07-15. Combining Transformers and CNNs for Efficient Object Detection in High-Resolution Satellite Imagery. https://arxiv.org/abs/2507.11040

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