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Arbitrary-Oriented Object Detection with Circular Smooth Label

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12353)

Abstract

Arbitrary-oriented object detection has recently attracted increasing attention in vision for their importance in aerial imagery, scene text, and face etc. In this paper, we show that existing regression-based rotation detectors suffer the problem of discontinuous boundaries, which is directly caused by angular periodicity or corner ordering. By a careful study, we find the root cause is that the ideal predictions are beyond the defined range. We design a new rotation detection baseline, to address the boundary problem by transforming angular prediction from a regression problem to a classification task with little accuracy loss, whereby high-precision angle classification is devised in contrast to previous works using coarse-granularity in rotation detection. We also propose a circular smooth label (CSL) technique to handle the periodicity of the angle and increase the error tolerance to adjacent angles. We further introduce four window functions in CSL and explore the effect of different window radius sizes on detection performance. Extensive experiments and visual analysis on two large-scale public datasets for aerial images i.e. DOTA, HRSC2016, as well as scene text dataset ICDAR2015 and MLT, show the effectiveness of our approach. The code is public available at https://github.com/Thinklab-SJTU/CSL_RetinaNet_Tensorflow.

Keywords

Oriented object detection Circular smooth label 

Notes

Acknowledgment

Corresponding author is Junchi Yan. The work is supported by National Key Research and Development Program of China (2018AAA0100704), National Natural Science Foundation of China (61972250, U19B2035). The author Xue Yang is partly supported by Wu Wen Jun Honorary Doctoral Scholarship, AI Institute, Shanghai Jiao Tong University.

Supplementary material

504445_1_En_40_MOESM1_ESM.zip (38.7 mb)
Supplementary material 1 (zip 39594 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.MoE Key Lab of Artificial Intelligence, AI InstituteShanghai Jiao Tong UniversityShanghaiChina

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