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Research on improved algorithm of object detection based on feature pyramid

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Abstract

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.

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Acknowledgments

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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  1. School of Data Science and Technology, North University of China, Taiyuan, 030051, China

    Pinle Qin, Chuanpeng Li, Jun Chen & Rui Chai

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  1. Pinle Qin
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  2. Chuanpeng Li
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Correspondence to Pinle Qin.

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Qin, P., Li, C., Chen, J. et al. Research on improved algorithm of object detection based on feature pyramid. Multimed Tools Appl 78, 913–927 (2019). https://doi.org/10.1007/s11042-018-5870-3

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