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Proceedings Paper

Small infrared target detection by data-driven proposal and deep learning-based classification
Author(s): Junhwan Ryu; Sungho Kim
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Paper Abstract

In this paper, we propose a data-driven proposal and deep-learning based classification scheme for small target detection. Previous studies have shown feasible performance using conventional computer vision techniques such as spatial and temporal filters. However, those are handcrafted approaches and are not optimized due to the nature of the application fields. Recently, deep learning has shown excellent performance for many computer vision problems, which motivates the deep learning-based small target detection. The proposed data-driven proposal and convolutional neural network (DDP-CNN) can generate possible target locations through the data-driven proposal and final targets are recognized through the classification network. According to the experimental results using drone database, the DDP-CNN shows 91% of train accuracy and 0.85 of average precision (AP) of the target detection.

Paper Details

Date Published: 23 May 2018
PDF: 10 pages
Proc. SPIE 10624, Infrared Technology and Applications XLIV, 106241J (23 May 2018); doi: 10.1117/12.2304677
Show Author Affiliations
Junhwan Ryu, Yeungnam Univ. (Korea, Republic of)
Sungho Kim, Yeungnam Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 10624:
Infrared Technology and Applications XLIV
Bjørn F. Andresen; Gabor F. Fulop; Charles M. Hanson; John Lester Miller; Paul R. Norton, Editor(s)

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