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Computer Vision Approaches to Detect Bean Defects

  • Peterson A. Belan
  • Robson A. G. de Macedo
  • Sidnei A. de AraújoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)

Abstract

In this work are proposed computer vision approaches to detect three of the main defects found in beans: broken, bored by insect (Acanthoscelides obtectus) and moldy. In addition, we describe a fast and robust segmentation step that is combined with the proposed approaches to compose a computer vision system (CVS) applicable to the Brazilian beans quality inspection process, to determine the type of the product. The proposed approaches constitute an important practical contribution since, although there are some papers in the literature addressing visual inspection of beans, none of them deals with defects. In the conducted experiments a low-cost equipment, composed by a table made in structural aluminum, a conveyor belt and an image acquisition chamber, was used to simulate the characteristics of an industrial environment. The CVS evaluation was performed in two modes: offline and online. In the offline mode, a database composed by 120 images of bean samples containing grains of different classes and with different defects was employed, while in the online mode the grains contained in a batch were spilled continuously in the conveyor belt of the equipment for the proposed CVS to perform the tasks of segmentation and detection of defects. In the experiments the CVS was able to process an image of 1280×720 pixels in approximately 2 s, with average hit rates of 99.61% (offline) and 97.78% (online) in segmentation, and 90.00% (offline) and 85.00% (online) in detecting defects.

Keywords

Computer vision Beans Defects Inspection Visual quality 

Notes

Acknowledgements

The authors would like to thank FAPESP – São Paulo Research Foundation (Processes 2017/05188-9 and 2019/18389-8) by financial support.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Peterson A. Belan
    • 1
  • Robson A. G. de Macedo
    • 1
  • Sidnei A. de Araújo
    • 1
    Email author
  1. 1.Informatics and Knowledge Management Graduate ProgramUniversidade Nove de Julho – UNINOVESão PauloBrazil

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