The goal of visual surface inspection is to analyze an object’s surface and detect defects by looking at it from different angles. Developments over the past years have made it possible to partially automate this process. Inspection systems use robots to move cameras and obtain pictures that are evaluated by image processing algorithms. Setting up these systems or adapting them to new models is primarily done manually. A key challenge is to define camera viewpoints from which the images are taken. The number of viewpoints should be as low as possible while still guaranteeing an inspection of the desired quality. System engineers define and evaluate configurations that are improved based on a time-consuming trial-and-error process leading to a sufficient, but not necessarily optimal, configuration. With the availability of 3D surface models defined by triangular meshes, this step can be done virtually. This paper presents a new scalable approach to determine a small number of well-placed camera viewpoints for optical surface inspection planning. The initial model is approximated by B-spline surfaces. A set of geometric feature functionals is defined and used for an adaptive, non-uniform surface sampling that is sparse in geometrically low-complexity areas and dense in regions of higher complexity. The presented approach is applicable to solid objects with a given 3D surface model. It makes camera viewpoint generation independent of the resolution of the triangle mesh, and it improves previous results considering number of viewpoints and their relevance.

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Inspection planning approaches so far have focused on automatically obtaining an optimal set of viewpoints required to cover a given object. While research has provided interesting results, the automatic inspection planning has still not been made a part of the everyday inspection system development process. This is mostly because the plans are difficult to verify and it is impossible to compare them to laboratory-developed plans. In this work, we give an overview of available generate-and-test approaches, evaluate their results for various objects and finally compare them to plans created by inspection system development experts. The comparison emphasizes both benefits and downsides of automated approaches and highlights problems which need to be tackled in the future in order to make the automated inspection planning more applicable.