Projecting Points to Axes: Oriented Object Detection via Point-Axis Representation

TL;DR

This paper proposes a novel point-axis representation for oriented object detection that improves accuracy by decoupling location and rotation, and introduces a new model called Oriented DETR for end-to-end detection.

Contribution

It introduces the point-axis representation and the max-projection and cross-axis losses, enabling more accurate and flexible oriented object detection without extra annotations.

Findings

Significant performance improvements on oriented detection benchmarks.

Effective decoupling of location and orientation enhances detection accuracy.

End-to-end training with Oriented DETR achieves state-of-the-art results.

Abstract

This paper introduces the point-axis representation for oriented object detection, emphasizing its flexibility and geometrically intuitive nature with two key components: points and axes. 1) Points delineate the spatial extent and contours of objects, providing detailed shape descriptions. 2) Axes define the primary directionalities of objects, providing essential orientation cues crucial for precise detection. The point-axis representation decouples location and rotation, addressing the loss discontinuity issues commonly encountered in traditional bounding box-based approaches. For effective optimization without introducing additional annotations, we propose the max-projection loss to supervise point set learning and the cross-axis loss for robust axis representation learning. Further, leveraging this representation, we present the Oriented DETR model, seamlessly integrating the DETR framework for precise point-axis prediction and end-to-end detection. Experimental results demonstrate significant performance improvements in oriented object detection tasks.