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Proceedings Paper

Multi-target detection with larger scale difference
Author(s): Guiyang Liu; Shengyang Li; Yuyang Shao; Mingfei Han
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Paper Abstract

The main contribution of this article is to solve the problem of detection of larger scale differences. Aiming at the problem of small target detection, we can better use the underlying features of the convolution network to construct the hyper convolution feature to achieve better detection and recognition effect. For larger scale target, by dilated convolution operation, the context information of different scales can be integrated into high-level feature information according to different receptive fields. In this experiment, we introduce the lightweight convolutional network, SqueezeNet, as the basic feature network. The network has small size, fast training speed and strong expression ability. In the experiment environment of single Titan X GPU card, the distribution of the migrated dataset can be better studied by increasing the size of batch images during training. After the pre-training of the VOC dataset, the migration training was carried out in the remote sensing image dataset, and the mAP of the detection of the 12 targets reached 0.937205, which reached a better level of detection result.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061D (9 August 2018); doi: 10.1117/12.2503160
Show Author Affiliations
Guiyang Liu, Univ. of Chinese Academy of Sciences (China)
Technology and Engineering Ctr. for Space Utilization (China)
Shengyang Li, Technology and Engineering Ctr. for Space Utilization (China)
Yuyang Shao, Technology and Engineering Ctr. for Space Utilization (China)
Mingfei Han, Univ. of Chinese Academy of Sciences (China)
Technology and Engineering Ctr. for Space Utilization (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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