23 May 2018 Small infrared target detection by data-driven proposal and deep learning-based classification
Author Affiliations +
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.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Junhwan Ryu, Junhwan Ryu, Sungho Kim, Sungho Kim, "Small infrared target detection by data-driven proposal and deep learning-based classification", Proc. SPIE 10624, Infrared Technology and Applications XLIV, 106241J (23 May 2018); doi: 10.1117/12.2304677; https://doi.org/10.1117/12.2304677
PROCEEDINGS
10 PAGES + PRESENTATION

SHARE
RELATED CONTENT

A saliency-based approach to detection of infrared target
Proceedings of SPIE (October 25 2013)
Multiple dim targets detection in infrared image sequences
Proceedings of SPIE (November 23 2014)
A method of infrared small targets detection based on local...
Proceedings of SPIE (September 26 2013)
Target detection threshold in noisy color imagery
Proceedings of SPIE (September 09 2001)
Analysis of a proposed infrared sensor focal plane
Proceedings of SPIE (September 15 1992)

Back to Top