Phase-Shifting Coder: Predicting Accurate Orientation in Oriented Object Detection
원문 초록
With the vigorous development of computer vision, oriented object detection has gradually been featured. In this paper, a novel differentiable angle coder named phase-shifting coder (PSC) is proposed to accurately predict the orientation of objects, along with a dual-frequency version (PSCD). By mapping the rotational periodicity of different cycles into the phase of different frequencies, we provide a unified framework for various periodic fuzzy problems caused by rotational symmetry in oriented object detection. Upon such a framework, common problems in oriented object detection such as boundary discontinuity and square-like problems are elegantly solved in a unified form. Visual analysis and experiments on three datasets prove the effectiveness and the potentiality of our approach. When facing scenarios requiring high-quality bounding boxes, the proposed methods are expected to give a competitive performance. The codes are publicly available at https://github.com/open-mmlab/mmrotate.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
RQR3D: Reparametrizing the regression targets for BEV-based 3D object detection
Arbitrary-Oriented Object Detection in Remote Sensing Images Based on Polar Coordinates
Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection
Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection
Multiple Object Stitching for Unsupervised Representation Learning
Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study
토론
이 논문과 연결된 포럼 글이 아직 없습니다.