Scheme (mathematics)

Object detection

Orientation (vector space)

Operating system

Constraint (computer-aided design)

Set (abstract data type)

Computer science

Code (set theory)

Minimum bounding box

Outlier

Source code

Pattern recognition (psychology)

Mathematics

Politics

Image (mathematics)

Political science

Object (grammar)

Mathematical analysis

Artificial intelligence

Geometry

Representation (politics)

Programming language

Bounding overwatch

Computer vision

In contrast to the generic object, aerial targets are often non-axis aligned with arbitrary orientations having the cluttered surroundings. Unlike the mainstreamed approaches regressing the bounding box orientations, this paper proposes an effective adaptive points learning approach to aerial object detection by taking advantage of the adaptive points representation, which is able to capture the geometric information of the arbitrary-oriented instances. To this end, three oriented conversion functions are presented to facilitate the classification and localization with accurate orientation. Moreover, we propose an effective quality assessment and sample assignment scheme for adaptive points learning toward choosing the representative oriented reppoints samples during training, which is able to capture the non-axis aligned features from adjacent objects or background noises. A spatial constraint is introduced to penalize the outlier points for roust adaptive learning. Experimental results on four challenging aerial datasets including DOTA, HRSC2016, UCAS-AOD and DIOR-R, demonstrate the efficacy of our proposed approach. The source code is availabel at: https://github.com/LiWentomng/OrientedRepPoints.

Oriented RepPoints for Aerial Object Detection