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arXiv · 2606.01118

Rank-Aware Quantile Activation for Motion-Robust Crop Segmentation in UAV Imagery

Abinav Kiran·Sravan Danda·Aditya Challa·Sougata Sen·Daya Sagar B S
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Abstract

Motion blur from high-speed UAV acquisition de-grades semantic segmentation on rare texture-dependent classes with high agronomic value. Standard CNNs rely on high-frequency magnitude features that blur destroys, causing statistical erasure of minority signals. We propose Dual Quantile Activation (QAct), a rank-aware block replacing magnitude gating with instance-level rank normalization. Evaluated onAgriculture-Vision 2021 across zero-shot and blur-supervised regimes at multiple severities, QAct is the dominant architectural factor: it delivers consistent mIoU gains over ReLU across both regimes and all severities, with strongest gains on rare structural and texture-dependent classes. Some dominant classes (water,planter skip) show mixed per-class performance under distillation. At moderate blur, zero-shot QAct outperforms distillation-trained ReLU; across all severities, Distill-QAct achieves best performance, confirming rank aware activation and blur-domain training are complementary robustness sources.

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Abinav Kiran, Sravan Danda, Aditya Challa, Sougata Sen, Daya Sagar B S. 2026-05-31. Rank-Aware Quantile Activation for Motion-Robust Crop Segmentation in UAV Imagery. https://arxiv.org/abs/2606.01118

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