Ellipse-ABC: An Efficient Geometry-Consistent Quadratic Representation for Oriented Object Detection in Remote-Sensing Images

Information from the abstract

Oriented object detection in remote-sensing imagery benefits from a geometric representation that remains well-defined across localization, label assignment, and post-processing. We present Ellipse-ABC, a YOLOv8-based detector that directly predicts the coefficients of a positive-definite quadratic form while retaining the original branch for center estimation. The resulting matrix supplies Gaussian-compatible localization supervision and is analytically decoded into standardized OBB geometry for task-aligned assignment, rotated non-maximum suppression, and benchmark reporting, thereby reducing representation changes across the pipeline. Primary accuracy is evaluated with polygon area IoU between predicted and ground-truth-oriented bounding boxes. After rerunning the native baseline under the same dataset-specific training and augmentation recipes used by the ABC configurations, the complete ABC + GWD configuration changes polygon-IoU mAP50/mAP50-95 by +2.16/−1.17 points on UCAS-AOD and −1.23/+0.50 points on HRSC2016 across seeds 0, 1, and 2. An ABC + ProbIoU loss control performs better than ABC + GWD on HRSC2016 but worse on UCAS-AOD, indicating that the geometry-loss choice is dataset-dependent. The representation adds 198 parameters and no more than 0.0018 GFLOPs at the evaluated input sizes. Five repeated batch-1 measurements nevertheless show a 6.6–8.8% end-to-end latency increase and higher peak memory on DOTA-v1.5. These results support Ellipse-ABC as a geometry-consistent representation with small model-level overhead, without implying a universal accuracy gain.

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Related topics: Advanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Remote-Sensing Image Classification