The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 809–816, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-809-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 809–816, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-809-2021

  28 Jun 2021

28 Jun 2021

A NOVEL METHOD FOR INSPECTION DEFECTS IN COMMERCIAL EGGS USING COMPUTER VISION

Y. M. Valencia1, J. J. Majin1, V. B. Taveira1, J. D. Salazar2, M. E. Stivanello3, L. C. Ferreira3, and M. R. Stemmer1 Y. M. Valencia et al.
  • 1Automation and Systems Department, Universidade Federal de Santa Catarina (UFSC), Florianopólis, SC, Brazil
  • 2Mechanical Engineering Department, Labmetro, Universidade Federal de Santa Catarina (UFSC), Florianópolis, SC, Brazil
  • 3Academic Department of Metal-Mechanics, Instituto Federal Santa Catarina (IFSC), Florianópolis, SC, Brazil

Keywords: Automatic inspection, Computational vision, Deep learning, Egg sorting, Food industry, Image processing

Abstract. The objective of this work is to compare the use of classical image processing approaches with deep learning approaches in a visual inspection system for defects in commercial eggs. Currently, many industries perform the detection of defects in eggs manually, this implies a large number of workers with long working hours who are exposed to visual fatigue and physical and mental discomfort. As a solution, this work proposes to develop an automatic inspection technique for defects in eggs using computer vision, capable of being operable in the industry. Different image processing approaches were evaluated in order to determine the best solution in terms of performance and processing time.