Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (2): 120-135.doi: 10.11871/jfdc.issn.2096-742X.2020.02.010

• Special Issue: Data Analysis Technology & Application • Previous Articles     Next Articles

Small Object Detection and Recognition Based onDeep Learning

Leng Jiaxu1,2,Liu Ying1,2,*()   

  1. 1. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100089, China
    2. Data Mining and High Performance Computing Lab, University of Chinese Academy of Sciences, Beijing 101400, China
  • Online:2020-04-20 Published:2020-06-03
  • Contact: Ying Liu E-mail:yingliu@ucas.ac.cn

Abstract:

[Objective] In this paper, we aim to improve the detection performance for small objects by considering the characteristics of small objects under deep learning-based detection frameworks. [Methods] This paper improves small object detection and recognition performance from different aspects, including feature fusion, context learning and attention mechanism. Since the features of the small object are not evident, a bidirectional feature fusion method is proposed to improve the feature expression capability for small objects. In addition, a novel method is proposed to improve the detection performance by using the context information of small objects. Furthermore, to better identify the categories of small objects, an attention transfer method is proposed to improve the recognition rate. [Results] Experimental results show that the three proposed methods can significantly improve the detection and recognition performance for small objects on public datasets. [Conclusions] The research on feature fusion, context utilization and attention mechanism is very valuable for improving small object detection in complex scenes.

Key words: small object detection, feature fusion, context learning, attention mechanism