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Proceedings Paper

An improved object detection algorithm based on depthwise separable convolutions
Author(s): Xiuyuan Yu; Qiliang Bao; Haolong Jia; Yu Li; Rui Qin
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Paper Abstract

Aiming at small objects detection such as unmanned aerial vehicle (UAV), this paper proposes a fast object detection algorithm based on depth wise separable convolutions. Firstly, the inverted residuals units based on depth wise convolutions and pointwise convolutions are used to construct a lightweight feature extraction network to improve the network’s speed. Secondly, the feature pyramid network is used to detect the five scale feature maps to improve the detection performance of small objects. Otherwise, we make an UAV dataset based on the urban background for training and testing of our experiments. The experimental results show that the improved method proposed in this paper can effectively improve the detection accuracy and real-time performance of UAVs in complex urban backgrounds, and the computation of network is greatly reduced, thereby making it possible to achieve object detection on embedded systems.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114272T (31 January 2020);
Show Author Affiliations
Xiuyuan Yu, Key Lab. of Optical Engineering (China)
Institute of Optics and Electronics (China)
Univ. of Chinese Academy of Sciences (China)
Qiliang Bao, Key Lab. of Optical Engineering (China)
Institute of Optics and Electronics (China)
Univ. of Chinese Academy of Sciences (China)
Haolong Jia, Key Lab. of Optical Engineering (China)
Institute of Optics and Electronics (China)
Univ. of Chinese Academy of Sciences (China)
Yu Li, Key Lab. of Optical Engineering (China)
Institute of Optics and Electronics (China)
Univ. of Chinese Academy of Sciences (China)
Rui Qin, Boltzmann Technology (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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