Multimedia Tools and Applications

, Volume 78, Issue 1, pp 913–927 | Cite as

Research on improved algorithm of object detection based on feature pyramid

  • Pinle QinEmail author
  • Chuanpeng Li
  • Jun Chen
  • Rui Chai


To solve the low detection accuracy of SSD for the small size object, this paper proposed an improved algorithm of SSD object detection based on the feature pyramid (FP-SSD). In the deep convolutional neural network, the high-level features contain well semantic information but are not sensitive to the translations. The low-level features have high resolutions but could not represent the features well. The feature pyramid structure contains multi-scale features. To combine the high and low-level features of the pyramid, the algorithm of this paper applied the deconvolution network to the high-level features of the feature pyramid to get the semantic information, dilated convolution network to learn the position information of the low-level features and used convolution for the middle level features to reduce the feature channels, then used convolution to fuse the features. After using the algorithm, a multi-scale detection structure is constructed. FP-SSD achieves a mean accuracy of 79% on PASCAL VOC2007, and 47% on MSCOCO, which has a great improve compared with SSD. We compared the detection accuracy and results with all kinds of scales by experiments, compared with SSD, the accuracy of FP-SSD is higher, which has more accurate location and higher recognition confidence.


Feature pyramid Object detection Convolutional neural network Multi-scale detection Deep learning 



This work is partially supported by Shanxi Science Foundation (No.2015011045). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Data Science and TechnologyNorth University of ChinaTaiyuanChina

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