<title>Active vision approach for optimizing illumination in critical surface inspection by machine vision</title>

The article presents a selected area of on-going research on the possibility of using intelligent computer-aided design systems for surface inspection of products manufactured in large batch processes. Systems based on machine vision are used wherever it is essential to obtain high efficiency, reproducibility, and where there is the need to use non-contact measurement methods. The IT solution significantly improves the design processes by automatically generating optimal configuration of machine vision systems intended for the detection of surface defects in manufactured products. General structure of the model and its individual modules, performing functions such as automatic component selection of machine vision systems, setting the angle of the light emission towards the surface of the analysed objects, and the selection of the range of light radiation are discussed. The individual configuration steps of vision systems for surface inspection, in which processes are implemented using expert systems making inferences based on both classical bivalent logic, as well as multi-valued fuzzy logic, are shown. The author presents the original methodology for the optimisation of structures forming vision systems intended for the reduction of components and costs associated with their implementation in physical structures, designed for use in production lines. The results of empirical studies of the calculation model are shown.

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REVIEW ON PHOTOGRAMMETRIC SURFACE INSPECTION IN AUTOMOTIVE PRODUCTION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2021-511-2021 ◽

2021 ◽

Vol XLIII-B2-2021 ◽

pp. 511-518

Author(s):

M. Hödel ◽

L. Hoegner ◽

U. Stilla

Keyword(s):

Deep Learning ◽

Machine Vision ◽

Data Driven ◽

Quality Data ◽

Surface Inspection ◽

Production Environment ◽

Production Chain ◽

Production Stages ◽

Early Production ◽

Automotive Production

Abstract. When purchasing a premium car for a substantial sum, first impressions count. Key to that first impression is a flawless exterior appearance, something self-explanatory for the customer, but a far greater challenge for production than one might initially assume. Fortunately, photogrammetric technologies and evaluation methods are enabling an ever greater degree of oversight in the form of comprehensive quality data at different automotive production stages, namely stamping, welding, painting and finishing. A drawback lies in the challenging production environment, which complicates inline integratability of certain technologies. In recent years, machine vision and deep learning have been applied to photogrammetric surface inspection with ever increasing success. Given comprehensive surface quality information throughout the entire production chain, production parameters can be dialed in ever tighter in a data-driven fashion, leading to a sustainable increase in quality. This paper provides a review of current and potential contributions of photogrammetry to this end, discussing several recent advances in research along the way. Particular emphasis will be placed on early production stages, as well as the application of machine vision and deep learning to this challenging task. An outline for further research conducted by the authors will conclude this paper.

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