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Single Fusion Image from Collections of Fruit Views for Defect Detection and Classification.

Antonio Albiol1, Carlos Sánchez de Merás1, Alberto Albiol2

  • 1Departamento de Comunicaciones, Universitat Politècnica de València, 46022 Valencia, Spain.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
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This study introduces a new method for fruit quality assessment, creating a single surface map from multiple views to reduce analysis time and prevent defect miscounts. This technique is easily integrated into industrial inspection systems for efficient defect detection.

Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Image Processing

Background:

  • Quality assessment in the agri-food industry often involves analyzing multiple fruit views, which is time-consuming and prone to redundant data.
  • Significant overlap between consecutive views can lead to miscounting defects, necessitating efficient data handling.

Purpose of the Study:

  • To develop a method for creating a unified fruit surface map from multiple rotational views.
  • To reduce the computational load and improve the accuracy of defect detection in fruit quality assessment.
  • To enable seamless integration into high-throughput industrial inspection systems.

Main Methods:

  • A novel methodology is presented to generate fruit surface maps by combining information from multiple views captured during rotation.
Keywords:
3Dfruitmappingprojectionquality assessmentrotationunwrapping

Related Experiment Videos

  • A simple geometrical model is assigned to each fruit, and 3D rotation between consecutive views is estimated directly from images.
  • No additional sensors or conveyor information are required, simplifying implementation.

    • Main Results:

    • The developed method successfully creates comprehensive surface maps, consolidating data from multiple views into a single representation.
    • The technique effectively reduces the number of analysis operations required for quality assessment.
    • The surface maps were utilized as input for a Convolutional Neural Network (CNN) to accurately classify oranges.

    Conclusions:

    • The proposed surface mapping technique offers an efficient and accurate solution for fruit quality assessment in industrial settings.
    • The method's ability to estimate rotation directly from images makes it adaptable to existing high-throughput inspection machines without hardware modifications.
    • This approach significantly enhances defect detection accuracy and reduces processing time, benefiting the agri-food industry.