AI 中文总结
针对遥感图像船舶检测中现有OBB转HBB方法的缺陷，提出形状感知转换方法，利用船体形状等信息生成更紧凑的轴对齐HBB，与三种基线方法对比验证性能。
AI 中文摘要
航空和卫星图像中的精确目标检测依赖于边界框表示，对于船舶、飞机等空间定向物体尤其如此。定向边界框（OBB）比水平边界框（HBB）拟合更紧密，非极大值抑制也更鲁棒，但当前从OBB到HBB的后处理转换要么引入过多空白和背景空间，要么丢失检测数据。本文提出一种用于遥感图像船舶检测的形状感知OBB到HBB转换新方法，该方法利用船体形状、船体饱满度和边界框方向生成更紧凑的轴对齐HBB表示。将该方法与三种OBB到HBB转换基线方法对比：采用最小最大值的外部HBB、面积等效HBB及GBB边缘化HBB。
英文摘要
Accurate object detection in aerial and satellite imagery is dependent upon the bounding box representation. This is especially true for spatially oriented objects such as ships or aircrafts. Oriented Bounding Boxes (OBB) have a tighter fit and more robust non-max suppression compared to Horizontal Bounding Boxes (HBB), any current post-processing conversion from OBB to HBB either introduces excess empty and background space or removes data from the detection. This paper introduces a novel approach for a shape-aware OBB-to-HBB conversion for ship detection in remote sensing imagery. It leverages hull shape, hull fullness, and the bounding box orientation to produce a tighter axis-aligned HBB representation. The proposed method is benchmarked against three baselines methods for OBBto-HBB conversion, Outer HBB which uses minimum and maximum, Area Equivalent HBB and GBB Marginalized HBB.
Comments8 pages
