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Proceedings Paper

Small and weak target detection based on deep learning
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Paper Abstract

In recent years, small and weak target detection technology is one of the hotspots in information processing technology. However, the detection precision and speed of weak targets still have yet to be improved.
As a branch of machine learning, deep learning has become more and more widely used in various fields. Therefore, this paper improves the deep convolutional networks for the characteristics of weak target detection, including the following three aspects:
Firstly, a dataset dedicated to small and weak target detection is established. The data is sufficient and representative, which is beneficial to improve the quality of the network model. Each image in the dataset has a corresponding label that indicates the name of the image, and the coordinates and width of the target circumscribed rectangle.
Secondly, the image is dilated many times so that the target having only a few pixels is covered by a lot of pixels. The highlighted portion of the image is dilated, and the result image has a larger highlighted area than the original image.
Thirdly, the Faster R CNN algorithm is improved. In this paper, by adjusting the learning rates, a suitable one is determined to get the best network model.
The results show that the average precision on the dataset has improved. The method proposed in this paper is of great significance for the detection of small and weak targets. For the military field, the research on weak target detection has high military value for improving early warning capability and counterattack capability.

Paper Details

Date Published: 18 December 2019
PDF: 6 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420I (18 December 2019); doi: 10.1117/12.2547652
Show Author Affiliations
Ting Wang, Xidian Univ. (China)
Changqing Cao, Xidian Univ. (China)
Xiaodong Zeng, Xidian Univ. (China)
Zhejun Feng, Xidian Univ. (China)
Yutao Liu, Xidian Univ. (China)
Jinna Ning, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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