Deconvolution single shot multibox detector for supermarket commodity detection and classification

Dejian Li; Jian Li; Binling Nie; Shouqian Sun

doi:10.1117/12.2281740

21 July 2017 Deconvolution single shot multibox detector for supermarket commodity detection and classification

Dejian Li, Jian Li, Binling Nie, Shouqian Sun

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104202R (2017) https://doi.org/10.1117/12.2281740
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

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.

Citation Download Citation

Dejian Li, Jian Li, Binling Nie, and Shouqian Sun "Deconvolution single shot multibox detector for supermarket commodity detection and classification", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202R (21 July 2017); https://doi.org/10.1117/12.2281740

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