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Deconvolution single shot multibox detector for supermarket commodity detection and classification
Author(s): Dejian Li; Jian Li; Binling Nie; Shouqian Sun
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Paper Abstract

This paper proposes an image detection model to detect and classify supermarkets shelves’ commodity. Based on the principle of the features directly affects the accuracy of the final classification, feature maps are performed to combine high level features with bottom level features. Then set some fixed anchors on those feature maps, finally the label and the position of commodity is generated by doing a box regression and classification. In this work, we proposed a model named Deconvolutiuon Single Shot MultiBox Detector, we evaluated the model using 300 images photographed from real supermarket shelves. Followed the same protocol in other recent methods, the results showed that our model outperformed other baseline methods.

Paper Details

Date Published: 21 July 2017
PDF: 5 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202R (21 July 2017); doi: 10.1117/12.2281740
Show Author Affiliations
Dejian Li, Zhejiang Univ. (China)
Jian Li, Nanjing Univ. of Science and Technology (China)
Binling Nie, Zhejiang Univ. (China)
Shouqian Sun, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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