R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

Authors

  • Xue Yang Department of Computer Science and Engineering, Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University
  • Junchi Yan Department of Computer Science and Engineering, Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University
  • Ziming Feng China Merchants Bank Credit Card Center
  • Tao He Anhui COWAROBOT CO., Ltd. Anhui Provincial Key Laboratory of Multimodal Cognitive Computation

Keywords:

Object Detection & Categorization

Abstract

Rotation detection is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. Though considerable progress has been made, for practical settings, there still exist challenges for rotating objects with large aspect ratio, dense distribution and category extremely imbalance. In this paper, we propose an end-to-end refined single-stage rotation detector for fast and accurate object detection by using a progressive regression approach from coarse to fine granularity. Considering the shortcoming of feature misalignment in existing refined single-stage detector, we design a feature refinement module to improve detection performance by getting more accurate features. The key idea of feature refinement module is to re-encode the position information of the current refined bounding box to the corresponding feature points through pixel-wise feature interpolation to realize feature reconstruction and alignment. For more accurate rotation estimation, an approximate SkewIoU loss is proposed to solve the problem that the calculation of SkewIoU is not derivable. Experiments on three popular remote sensing public datasets DOTA, HRSC2016, UCAS-AOD as well as one scene text dataset ICDAR2015 show the effectiveness of our approach. The source code is available at https://github.com/Thinklab-SJTU/R3Det_Tensorflow and is also integrated in our open source rotation detection benchmark: https://github.com/yangxue0827/RotationDetection.

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Published

2021-05-18

How to Cite

Yang, X., Yan, J., Feng, Z., & He, T. (2021). R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object. Proceedings of the AAAI Conference on Artificial Intelligence, 35(4), 3163-3171. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16426

Issue

Section

AAAI Technical Track on Computer Vision III