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arXiv Computer Vision By Fenghao Song, Shaojing Yang, Xi Zhou

SARES-DEIM: Sparse Mixture-of-Experts Meets DETR for Clutter-Aware SAR Ship Detection

Read the original on arXiv Computer Vision →

SARES-DEIM is a SAR ship detection framework that builds on the DETR architecture. It introduces a sparse mixture‑of‑experts module, SARESMoE, which routes features to wavelet, spatial, and frequency experts, and a Space‑to‑Depth Enhancement Pyramid (SDEP) neck that preserves high‑resolution cues for small targets. The method achieves state‑of‑the‑art performance on HRSID and SAR‑Ship‑Dataset, delivering 76.4% mAP50:95 and 93.8% mAP50 while maintaining efficient inference at 136.5 FPS on an NVIDIA A40 GPU.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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