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Proceedings Paper

Infrared polymorphic target recognition based on single step cascade neural network
Author(s): Zhuangzhuang Hu; Hanlin Qin; Xin Peng; Tong Yue; Heng Yue; Guohui Luo; Wenrui Zhu
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Paper Abstract

Infared ship recognition has many applications in port supervision and management. However, when the imaging distance is long or the target changes are obvious, it is difficult to achieve accurate detection and recognition by traditional methods. In this paper, we designed a single step cascade neural network that consists of three parts: feature extraction module, scale transform module and classification regression module. Firstly, the VGG network is used to extract the different level features of the target images. Then the scale transform module is used to fuse the high-level features and the low-level features to reflect the semantic information and shallow information of the targets more completely. The generated region of interest is input to classification regression module that predicts the targets location and classes. The main contribution of this paper is to combine the specific problems of infrared polymorphic ships detection and recognition. The clustering algorithm is used to generate the appropriate anchors to adapt our targets, and the attention mechanism is introduced into the model training process. Compared with the traditional detection and recognition methods, the proposed single step cascade neural network achieves the better average precision in polymorphic ships.

Paper Details

Date Published: 18 December 2019
PDF: 6 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420T (18 December 2019); doi: 10.1117/12.2548172
Show Author Affiliations
Zhuangzhuang Hu, Xidian Univ. (China)
Hanlin Qin, Xidian Univ. (China)
Xin Peng, Xidian Univ. (China)
Tong Yue, Xidian Univ. (China)
Heng Yue, Xidian Univ. (China)
Guohui Luo, Xidian Univ. (China)
Wenrui Zhu, 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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