Precise Orientation Estimation for Rotated Object Detection Based on a Unit Vector Coding Approach

Abstract

Existing rotated object detection methods usually use angular parameters to represent the object orientation. However, due to the symmetry and periodicity of these angular parameters, a well-known boundary discontinuity problem often results. More specifically, when the object orientation angle approaches the periodic boundary, the predicted angle may change rapidly and adversely affect model training. To address this problem, this paper introduces a new method that can effectively solve the boundary discontinuity problem related to angle parameters in rotated object detection. Our approach involves a novel vector-based encoding and decoding technique for angular parameters, and a cosine distance loss function for angular accuracy evaluation. By utilizing the characteristics of unit vectors and cosine similarity functions, our method parameterizes the orientation angle as components of the unit vector during the encoding process and redefines the orientation angle prediction task as a vector prediction problem, effectively avoiding the boundary discontinuity problem. The proposed method achieved a mean average precision (mAP) of 87.48% and an average cosine similarity (CS) of 0.997 on the MVTec test set. It also achieved an mAP score of 90.54% on the HRSC2016 test set, which is better than several existing state-of-the-art methods and proves its accuracy and effectiveness.