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Proceedings Paper

TLCS-Anchor: a new anchor strategy for detecting small-scale unmanned aerial vehicle
Author(s): Tao Xiong; Jing Hu; Xinxin Lu; Kan Jiang; Xiangjun Li
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Paper Abstract

Faster R-CNN is a general-purpose detection algorithm that performs well in most cases. However, Faster R-CNN performs poorly on detecting small-scale UAVs. In order to improve the detection performance for small-scale UAVs, a new anchor strategy (TLCS-Anchor) which could be adopted by Faster R-CNN is proposed in this paper. Firstly, the anchor templates are designed to be suitable for the UAV dataset by using the clustering method so that the aspect ratios and scales for anchors are more targeted to UAVs. Then, a new compensation strategy of anchors is proposed to help detect small-scale UAVs in this paper, which could not only improve the number of anchors matched with the UAVs, but also alleviate the problem that small-scale UAVs can’t match with enough anchors to some extent. Experimental results show that TLCS-Anchor can help improve the detection performance for UAVs, especially for small-scale UAVs. In theory, TLCS-Anchor can also be used to detect other small-scale targets.

Paper Details

Date Published: 14 February 2020
PDF: 9 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301S (14 February 2020); doi: 10.1117/12.2541789
Show Author Affiliations
Tao Xiong, Huazhong Univ. of Science and Technology (China)
Jing Hu, National Key Lab. of Science and Technology on Multi-Spectral Information Processing (China)
Xinxin Lu, Huazhong Univ. of Science and Technology (China)
Kan Jiang, Huazhong Univ. of Science and Technology (China)
Xiangjun Li, Yan'an Univ. (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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