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A Method of Small Object Detection Based on Improved Deep Learning

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Abstract

In this paper, a parallel SSD (Single Shot MultiBox Detector) fusion network based on inverted residual structure (IR-PSN) is proposed to solve the problems of the lack of extracted feature information and the unsatisfactory effect of small object detection by deep learning. Firstly, the Inverted Residual Structure (IR) is adopted into the SSD network to replace the pooling layer. The improved SSD network is called deep network of IR-PSN to extract high-level feature information of the image. Secondly, a shallow network based on the inverted residual structure is constructed to extract low-level feature information of the image. Finally, the shallow network is fused with the deep network to avoid the lack of small object feature information and improve the detection rate of small object. The experimental results show that the proposed method has satisfied results for small object detection under the premise of ensuring the accuracy rate P and recall rate R of the comprehensive object detection.

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Funding

This work was financially supported by the National Natural Science Foundation of China (Grant nos. 6154055, 61863011), the Dean Project of Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, China, the Science Research and Technology Development Program of Hezhou (no. 1707041).

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Authors and Affiliations

  1. School of Information and Communication, GuiLin University of Electronic Technology, Guangxi, China

    Changgeng Yu & Kai Liu

  2. School of Information and Communication Engineering, Hezhou University, Guangxi, China

    Changgeng Yu

  3. School of Mechatronic Engineering, Jiangsu Normal University, Xuzhou, China

    Wei Zou

Authors
  1. Changgeng Yu
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  2. Kai Liu
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  3. Wei Zou
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Corresponding author

Correspondence to Changgeng Yu.

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The authors declare that they have no conflicts of interest.

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Changgeng Yu, Liu, K. & Zou, W. A Method of Small Object Detection Based on Improved Deep Learning. Opt. Mem. Neural Networks 29, 69–76 (2020). https://doi.org/10.3103/S1060992X2002006X

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