Image Processing based on Deep Neural Networks for Detecting Quality Problems in Paper Bag Production
Anna Syberfeldt, Fredrik Vuoluterä
Abstract
It is critical for manufacturers to identify quality issues in production and prevent defective products being delivered to customers. We investigate the use of deep neural networks to perform automatic quality inspections based on image processing to eliminate the current manual inspection. A deep neural network was implemented in a real-world industrial case study, and its ability to detect quality problems was evaluated and analyzed. The results show that the network has an accuracy of 94.5%, which is considered good in comparison to the 70–80% accuracy of a trained human inspector.
Topics & Concepts
Artificial neural networkArtificial intelligenceQuality (philosophy)Production (economics)Computer scienceImage processingImage (mathematics)Deep neural networksImage qualityComputer visionEngineeringPattern recognition (psychology)EpistemologyMacroeconomicsEconomicsPhilosophyIndustrial Vision Systems and Defect DetectionSurface Roughness and Optical MeasurementsSpectroscopy and Chemometric Analyses