Translator Disclaimer
Paper
16 July 2019 Visual inspection for metallic surfaces: CNN driven by features
Author Affiliations +
Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 111720H (2019) https://doi.org/10.1117/12.2521455
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France
Abstract
In this paper, an effective and novel automatic learning solution for the quality control of metallic objects surfaces is proposed, which can be seamlessly integrated into the industrial process. Such a system requires a coaxial illuminator to capture the object view with a single camera while lighting it with structured light: in this way, the object surface can be viewed in time as a dynamic scene under different illumination conditions. By relying on a linear model to describe the expected evolution of the light over the object of interest, the Residuals of Linear Evolution of Light (RLEL) algorithm is derived with the specific aim of identifying and characterizing anomalies and defects through the residuals of a least square approach. Then, a novel learning strategy is developed that exploits the model-based RLEL descriptor and thus promotes itself as an alternative strategy to the black box approach of Convolutional Neural Networks (CNNs). By combining both the data-driven and the model-based learning approaches to perform the inspection task, an Hybrid Learning (HL) procedure is defined: in a first phase, the HL exploits an Encoder-Decoder network to incorporate the model-based description while, in a second phase, it uses only the pre-trained encoder to drive the learning process of a 3D-CNN. In doing so, the proposed procedure reaches interesting results that exceed also the performance of state-of-the-art 3D-Inception and 3D-Residual networks.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Riccardo Fantinel and Angelo Cenedese "Visual inspection for metallic surfaces: CNN driven by features", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720H (16 July 2019); https://doi.org/10.1117/12.2521455
PROCEEDINGS
8 PAGES


SHARE
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RELATED CONTENT

Semantic segmentation approach for tunnel roads’ analysis
Proceedings of SPIE (September 06 2019)
Multi-loss joint optimization for person re-identification
Proceedings of SPIE (November 27 2019)
Model-Based Vision: Using Cues To Select Hypotheses
Proceedings of SPIE (November 17 1986)
Model-based tracking using a real-time laser rangefinder
Proceedings of SPIE (November 29 1993)
Refinding objects using model-based knowledge
Proceedings of SPIE (October 05 2001)

Back to Top