Optical quality control using deep learning

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

1 Citation (Scopus)


Optical quality control is still often performed by people and always carries the risk of human error. A modern approach in order to solve this issue is the usage of artificial intelligence to boost performance and reliability. This paper focuses on implementing a prototype for optical quality control based on the YOLOv3 algorithm. This is a state-of-the-art object detection system that uses deep learning to detect different classes of objects within an image. Instead of different kinds of objects, the classes in this prototype were different quality levels of a strawberry. The dataset for this task was gathered by taking photos and using images from the internet. The strawberries on these images were labeled and fed to the YOLOv3 algorithm for training. Despite the poor detection rate, the results showed that it is generally possible to use such systems for detecting different quality levels of products.

Original languageEnglish
Title of host publication31st European Modeling and Simulation Symposium, EMSS 2019
EditorsMichael Affenzeller, Agostino G. Bruzzone, Francesco Longo, Guilherme Pereira
Number of pages9
ISBN (Electronic)9788885741263
Publication statusPublished - 2019
Event31st European Modeling and Simulation Symposium, EMSS 2019 - Lisbon, Portugal
Duration: 18 Sept 201920 Sept 2019

Publication series

Name31st European Modeling and Simulation Symposium, EMSS 2019


Conference31st European Modeling and Simulation Symposium, EMSS 2019


  • Artificial intelligence
  • Deep learning
  • Neural networks
  • Object detection
  • Optical quality control


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