CoLR-Det: Collaborative Latent Restoration for Small Object Detection in Low-Resolution Remote Sensing Images
arXiv:2601.12507v2 Announce Type: replace-cross Abstract: Low-resolution remote sensing small object detection is limited by both missing visual details and the ambiguity of how details serve detection. Existing super-resolution-assisted detectors generally follow a restoration-first paradigm to explicitly enhance inputs before detection, which implicitly assumes visual fidelity benefits recognition. Yet super-resolution favors dense texture and edge recovery, while object detection relies on sparse instance-level semantics, making restoration amplify visually plausible but semantically irrele
This research addresses a fundamental challenge in remote sensing, which is increasingly critical for various industries as data acquisition methods improve.
Improving small object detection in low-resolution remote sensing images has significant implications for defense, urban planning, agriculture, and environmental monitoring.
The proposed CoLR-Det method offers a more effective approach to combine image restoration with object detection, potentially leading to higher accuracy in real-world applications.
- · Defense contractors
- · Satellite imagery providers
- · AI/ML research labs
- · Remote sensing analytics companies
- · Traditional image enhancement algorithms
- · Competitors with less efficient object detection methods
Enhanced capabilities for surveillance and asset tracking through improved small object detection.
More efficient and accurate monitoring of large geographical areas, impacting resource management and strategic decision-making.
Potential for new autonomous systems that rely on highly granular remote sensing data for navigation and task execution.
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Read at arXiv cs.LG