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A maritime targets detection method based on hierarchical and multi-scale deep convolutional neural network
Author(s): Wei Chen; Juelong Li; Jianchun Xing; Qiliang Yang; Qizhen Zhou
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Paper Abstract

The mainstream detection methods Faster R-CNN and SSD are mainly designed for general dataset, but do not emphasize the detection effect of small targets and can not to achieve higher average detection accuracy on general dataset. In order to overcome the problem, we present a target detection method based on hierarchical and multi-scale convolutional neural network aiming at the detection task of maritime targets in complex scenario. To enhance the detection capability of small targets, we extract proposals of different scales in the multi-resolution convolution feature map in the region proposal network. To further improve the detection accuracy, we add an object detection network. The convolution feature maps with high-resolution are used to extract the targets, then an upsampling layer is added to enhance the resolution of the feature maps. The region proposal network and object detection network are then combined to realize the accurate detection of the target. The experiment results demonstrate that the proposed method achieves good detection results in maritime targets dataset, and the accuracy of target detection outperforms those of the mainstream detection methods.

Paper Details

Date Published: 9 August 2018
PDF: 9 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080616 (9 August 2018); doi: 10.1117/12.2503030
Show Author Affiliations
Wei Chen, Army Engineering Univ. of PLA (China)
Juelong Li, Research Ctr. of Coastal Defense Engineering (China)
Jianchun Xing, Army Engineering Univ. of PLA (China)
Qiliang Yang, Army Engineering Univ. of PLA (China)
Qizhen Zhou, Army Engineering Univ. of PLA (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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