aiqbench analysis
This research addresses a critical bottleneck in remote sensing computer vision by proposing a shape-aware method to convert oriented bounding boxes (OBB) into horizontal ones without losing detection data or introducing excessive background noise. By leveraging hull geometry and orientation, the technique improves upon existing baselines for tracking spatially aligned objects like ships and aircrafts in aerial imagery. The work advances object representation efficiency specifically within machine learning pipelines for satellite analysis rather than general consumer applications.
Shape-Aware Oriented Bounding Box (OBB) to Horizontal Bounding Box (HBB) Conversion
arXiv:2608.05858v1 Announce Type: cross Abstract: 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.
Abstract: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.
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
arXiv:2608.05858 [cs.CV]
(or arXiv:2608.05858v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.05858 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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