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Proceedings Paper

Deep learning application: rubbish classification with aid of an android device
Author(s): Sijiang Liu; Bo Jiang; Jie Zhan
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Paper Abstract

Deep learning is a very hot topic currently in pattern recognition and artificial intelligence researches. Aiming at the practical problem that people usually don’t know correct classifications some rubbish should belong to, based on the powerful image classification ability of the deep learning method, we have designed a prototype system to help users to classify kinds of rubbish. Firstly the CaffeNet Model was adopted for our classification network training on the ImageNet dataset, and the trained network was deployed on a web server. Secondly an android app was developed for users to capture images of unclassified rubbish, upload images to the web server for analyzing backstage and retrieve the feedback, so that users can obtain the classification guide by an android device conveniently. Tests on our prototype system of rubbish classification show that: an image of one single type of rubbish with origin shape can be better used to judge its classification, while an image containing kinds of rubbish or rubbish with changed shape may fail to help users to decide rubbish’s classification. However, the system still shows promising auxiliary function for rubbish classification if the network training strategy can be optimized further.

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104431P (19 June 2017);
Show Author Affiliations
Sijiang Liu, Nanjing Univ. of Posts and Telecommunications (China)
Bo Jiang, Nanjing Univ. of Posts and Telecommunications (China)
Jie Zhan, Nanjing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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