水面反光会严重干扰海上障碍物检测，导致误检率上升1.2至9.6个百分点。
Impact of Surface Reflections in Maritime Obstacle Detection
- 通过对比带反光与去反光图像，量化反光对检测器性能的影响。
- 提出基于热图的滑动滤波方法，误检减少34.64%且几乎不影响真阳性。
- 公开两个自建数据集，适合研究海上视觉感知与鲁棒性优化者使用。
海上障碍物检测旨在为无人水面车辆自主驾驶识别潜在障碍。在特定条件下，水面可像镜子一样产生影像反射，此前研究已指出反射是导致海洋障碍物检测中误报的重要原因。本文证实反射确实显著降低检测器性能。我们通过两个自建数据集测试其影响：一个包含反射图像，另一个经修补处理去除了反射。实验显示，反射使mAP下降1.2至9.6点，不同检测器表现各异。为此，我们提出一种名为基于热图的滑动滤波（Heatmap Based Sliding Filter）的新过滤方法。结果表明，该方法可减少34.64%的总误报，同时对真阳性影响极小。定性分析也验证了该方法有效消除反射区域的误检。相关数据集已开源，地址为 https://github.com/SamedYalcin/MRAD。
原文摘要 · Abstract (English)
Maritime obstacle detection aims to detect possible obstacles for autonomous driving of unmanned surface vehicles. In the context of maritime obstacle detection, the water surface can act like a mirror on certain circumstances, causing reflections on imagery. Previous works have indicated surface reflections as a source of false positives for object detectors in maritime obstacle detection tasks. In this work, we show that surface reflections indeed adversely affect detector performance. We measure the effect of reflections by testing on two custom datasets, which we make publicly available. The first one contains imagery with reflections, while in the second reflections are inpainted. We show that the reflections reduce mAP by 1.2 to 9.6 points across various detectors. To remove false positives on reflections, we propose a novel filtering approach named Heatmap Based Sliding Filter. We show that the proposed method reduces the total number of false positives by 34.64% while minimally affecting true positives. We also conduct qualitative analysis and show that the proposed method indeed removes false positives on the reflections. The datasets can be found on https://github.com/SamedYalcin/MRAD.
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