English(EN) Strip Convolution and Direction-Aware Exclusion Loss for Oriented Ship Detection

新方法改进遥感影像中的定向船舶检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了一种在极高分辨率遥感影像中检测定向船舶的新方法，解决了细长船体形状和密集目标分布带来的挑战。该方法引入了一个利用正交条形卷积来更好地捕捉船体结构的 C3k2_Strip 模块。此外，还采用了一个类别感知方向感知排斥损失 (CA-DAEL) 函数，通过考虑类别、方向和置信度线索来减少冗余预测。在 HRSC2016 和 DIOR-R 数据集上的实验显示出显著的改进，该方法分别在 HRSC2016 和 DIOR-R 上实现了 78.45% 和 53.71% 的 mAP50:95，并且在 HRSC2016 上比 YOLOv11-OBB 基线高出 6.32 个百分点。 AI

影响增强了海事监控和港口监测等专业应用的目标检测能力。

排序理由该集群包含一篇详细介绍特定计算机视觉任务新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Bin Chen, Yuanyuan Liu, Peng Yang, Chao Lu · 2026-10-16 04:00

用于定向船舶检测的条纹卷积和方向感知排除损失

arXiv:2609.27238v2 Announce Type: replace Abstract: Oriented ship detection in very high resolution (VHR) remote sensing imagery remains challenging due to elongated hull geometry and dense target distributions in complex port scenes. Existing methods typically address geometric …

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用于定向船舶检测的条纹卷积和方向感知排除损失

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