1. Proceedings
  2. I3M
  3. 2019
  4. EMSS
  5. 10.46354/i3m.2019.emss.019

Optical quality control using deep learning

  • a Franz Wiesinger  ,
  • b Daniel Klepatsch  ,
  • c Michael Bogner  
  • a,b,c University of Applied Sciences Upper Austria – Department of Embedded Systems Engineering, Softwarepark 11, A-4232 Hagenberg, Austria
Cite as
Wiesinger F., Klepatsch D., Bogner M. (2019). Optical quality control using deep learning. Proceedings of the 31st European Modeling & Simulation Symposium (EMSS 2019), pp. 119-127. DOI: https://doi.org/10.46354/i3m.2019.emss.019.

Abstract

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.

References

  1. Hijazi S. et al., 2015. Using convolutional neural networks for image recognition. Available from: https://ip.cadence.com/uploads/901/cnn_wp-pdf [accessed 8 April 2019]
  2. Redmon J. et al., 2016. You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, 779-788. June 8-10, Boston, Massachusetts.
  3. Redmon J. et al., 2017. YOLO9000: better, faster, stronger. Proceedings of the IEEE conference on computer vision and pattern recognition, 7263-7271. July 22-25, Honolulu, Hawaii.
  4. Redmon J. et al., 2018. YOLOv3: An Incremental Improvement. Available from: https://arxiv.org/pdf/1804.02767.pdf [accessed 8 April 2019]
  5. Gschwend D., 2016. ZynqNet: An FPGA-Accelerated Embedded Convolutional Neural Network. Master Thesis. ETH Zürich.
  6. Ng A., 2017. C4W3L01 Object Localization. Available from: https://www.youtube.com/watch?v=GSwYGkTfOKk [accessed 8 April 2019]
  7. Henderson P., Ferrari V., 2016. End-to-end training of object class detectors for mean average precision. Proceedings of the Asian Conference on Computer Vision, 198-213. November 21-23, Taipei, Taiwan.