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Regular and Small Target Detection

  • Wenzhe WangEmail author
  • Bin WuEmail author
  • Jinna Lv
  • Pilin Dai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11296)

Abstract

Although remarkable results have been achieved in the areas of object detection, the detection of small objects is still a challenging task now. The low resolution and noisy representation make small objects difficult to detect, and further recognition will be much harder. Aiming at the small objects that have regular positions, shapes, colors or other features, this paper proposes an approach of Regular and Small Target Detection based on Faster R-CNN (RSTD) for the detection and recognition of regular and small targets such as traffic signs. In this approach, a regular and small target feature extraction layer is designed to automatically extract the surrounding background and internal key information of the proposal objects, which benefits the detection and recognition. Extensive evaluations on Tsinghua-Tencent 100K and GTSDB datasets demonstrate the superiority of our approach in detecting traffic signs over well-established state-of-the-arts. The source code and model introduced in this paper are publicly available at: https://github.com/zhezheey/RSTD/.

Keywords

Regular and small target Traffic sign detection Traffic sign recognition Faster R-CNN 

Notes

Acknowledgement

This work is partially supported by the National Key R&D Program of China (No. 2018YFC0831500), the National Social Science Foundation of China (No. 16ZDA055), the National Natural Science Foundation of China (No. 61772082), and the Special Found for Beijing Common Construction Project.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Intelligent Telecommunication Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina

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