TY - GEN
T1 - Optical quality control using deep learning
AU - Wiesinger, Franz Leopold
AU - Klepatsch, Daniel
AU - Bogner, Michael
N1 - Publisher Copyright:
© 2019 Dime Universita di Genova, DIMEG University of Calabria.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Deep learning
KW - Neural networks
KW - Object detection
KW - Optical quality control
UR - http://www.scopus.com/inward/record.url?scp=85073773832&partnerID=8YFLogxK
M3 - Conference contribution
T3 - 31st European Modeling and Simulation Symposium, EMSS 2019
SP - 119
EP - 127
BT - 31st European Modeling and Simulation Symposium, EMSS 2019
A2 - Affenzeller, Michael
A2 - Bruzzone, Agostino G.
A2 - Longo, Francesco
A2 - Pereira, Guilherme
PB - DIME UNIVERSITY OF GENOA
T2 - 31st European Modeling and Simulation Symposium, EMSS 2019
Y2 - 18 September 2019 through 20 September 2019
ER -