Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (7): 1649-1660.DOI: 10.3778/j.issn.1673-9418.2109081

• Graphics and Image • Previous Articles     Next Articles

Multi-scale Selection Pyramid Networks for Small-Sample Target Detection Algorithms

PENG Hao1, LI Xiaoming1,2,+()   

  1. 1. College of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
    2. Key Laboratory of Computer Science, College of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
  • Received:2021-08-17 Revised:2021-10-21 Online:2022-07-01 Published:2021-11-03
  • Supported by:
    the National Natural Science Foundation of China(61373099)


彭豪1, 李晓明1,2,+()   

  1. 1.太原科技大学 计算机科学与技术学院,太原 030024
    2.太原科技大学 计算机科学与技术学院 计算机重点实验室,太原 030024
  • 作者简介:彭豪(1995—),男,山西运城人,硕士研究生,主要研究方向为深度学习。
    PENG Hao, born in 1995, M.S. candidate. His research interest is deep learning.
    LI Xiaoming, born in 1965, Ph.D., professor. His research interests include image processing and analysis, computer vision, etc.
  • 基金资助:


Target detection is to detect the specified target in the image. This technology has been widely used in automatic driving, face recognition and other fields, and has become a major research hotspot in the field of computer vision at home and abroad. Traditional target detection often requires a large number of annotated datasets, so it is a challenge to detect targets with only a small number of annotated samples. To address this problem, this paper proposes a multi-scale selection pyramid network algorithm for small sample target detection so that detection no longer relies on large-scale labeled datasets. Firstly, this paper designs a multi-scale selection pyramid network for small sample target detection, which consists of three components: context layer attention module, feature scale enhancement module, and feature scale selection module. Secondly, this paper performs feature fusion after the RoI features generated by the RPN network using maximum pooling and average pooling to improve the correlation between features. This paper uses feature subtraction to highlight the category information in the features, which can improve the sensitivity to new class parameters while maintaining the stability of the model to the sample parameters. Finally, the orthogonal mapping loss function is used to constrain the features before the classification layer, which can well measure the similarity between features even in the case of a small number of samples.

Key words: target detection, small sample, orthogonal mapping loss, feature fusion



关键词: 目标检测, 小样本, 正交映射损失, 特征融合

CLC Number: