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Convolutional neural networks for computer vision-based detection and recognition of dumpsters

  • Iván Ramírez
  • Alfredo Cuesta-Infante
  • Juan J. Pantrigo
  • Antonio S. Montemayor
  • José Luis Moreno
  • Valvanera Alonso
  • Gema Anguita
  • Luciano Palombarani
S.I. : IWINAC 2015

Abstract

In this paper, we propose a twofold methodology for visual detection and recognition of different types of city dumpsters, with minimal human labeling of the image data set. Firstly, we carry out transfer learning by using Google Inception-v3 convolutional neural network, which is retrained with only a small subset of labeled images out of the whole data set. This first classifier is then improved with a semi-supervised learning based on retraining for two more rounds, each one increasing the number of labeled images but without human supervision. We compare our approach against both to a baseline case, with no incremental retraining, and the best case, assuming we had a fully labeled data set. We use a data set of 27,624 labeled images of dumpsters provided by Ecoembes, a Spanish nonprofit organization that cares for the environment through recycling and the eco-design of packaging in Spain. Such a data set presents a number of challenges. As in other outdoor visual tasks, there are occluding objects such as vehicles, pedestrians and street furniture, as well as other dumpsters whenever they are placed in groups. In addition, dumpsters have different degrees of deterioration which may affect their shape and color. Finally, 35% of the images are classified according to the capacity of the container, which contains a feature which is hard to assess in a snapshot. Since the data set is fully labeled, we can compare our approach both against a baseline case, doing only the transfer learning using a minimal set of labeled images, and against the best case, using all the labels. The experiments show that the proposed system provides an accuracy of 88%, whereas in the best case it is 93%. In other words, the method proposed attains 94% of the best performance.

Keywords

Deep learning Dumpsters Semi-supervised learning Transfer learning 

Notes

Acknowledgements

This research has been supported by the Spanish Government research funding TIN-2015-69542-C2-1-R(MINECO/FEDER) and the Banco de Santander funding grant for the Computer Vision and Image Processing (CVIP) Excellence research group.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Iván Ramírez
    • 1
  • Alfredo Cuesta-Infante
    • 1
  • Juan J. Pantrigo
    • 1
  • Antonio S. Montemayor
    • 1
  • José Luis Moreno
    • 2
  • Valvanera Alonso
    • 2
  • Gema Anguita
    • 2
  • Luciano Palombarani
    • 2
  1. 1.School of Computer ScienceUniversidad Rey Juan CarlosMóstoles, MadridSpain
  2. 2.Dirección Técnica e Innovación & Dirección de RSC y SistemasEcoembalajes España, S.A. (ECOEMBES)MadridSpain

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