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Hauptverfasser: Yalçın, Samed, Ekenel, Hazım Kemal
Format: Preprint
Veröffentlicht: 2024
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Online-Zugang:https://arxiv.org/abs/2410.08713
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_version_ 1866914970100301824
author Yalçın, Samed
Ekenel, Hazım Kemal
author_facet Yalçın, Samed
Ekenel, Hazım Kemal
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2410_08713
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Impact of Surface Reflections in Maritime Obstacle Detection
Yalçın, Samed
Ekenel, Hazım Kemal
Computer Vision and Pattern Recognition
I.5.4
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.
title Impact of Surface Reflections in Maritime Obstacle Detection
topic Computer Vision and Pattern Recognition
I.5.4
url https://arxiv.org/abs/2410.08713