Flexible Surface Inspection Planning Pipeline

Gospodnetić, Petra; Mosbach, Dennis; Rauhut, Markus; Hagen, Hans

doi:10.1109/iccar49639.2020.9107983

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…Since then, the topic received attention sporadically, with most of the works focusing on optimal device placement. Works which did not focus solemnly on device placement were Tarabanis et al [2], Scott et al [3], and Gospodnetic et al [4]. Tarabanis et al [2] introduced the classification into generate-and-test and synthesis approach, and made an exhaustive overview of the work done so far.…”

Section: Inspection Planning Researchmentioning

confidence: 99%

“…Besides a survey, Scott et al [3] introduced 16 requirements a view planning algorithm for range sensing should satisfy and, among others, emphasized the lack of standardized benchmarks. Those requirements have been adapted and expanded by Gospodnetic et al [4], in order to be suitable for flexible inspection systems. Additionally, they introduced a concept of a modular inspection planning pipeline which is application-flexible and made a clear distinction between an automated and semi-automated inspection planning and posed a list of requirements flexible inspection planning systems must satisfy.…”

Section: Inspection Planning Researchmentioning

confidence: 99%

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Viewpoint placement for inspection planning

Gospodnetić

Mosbach

Rauhut

et al. 2021

Machine Vision and Applications

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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.

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Section: Inspection Planning Researchmentioning

confidence: 99%

Section: Inspection Planning Researchmentioning

confidence: 99%

Viewpoint placement for inspection planning

Gospodnetić

Mosbach

Rauhut

et al. 2021

Machine Vision and Applications

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“…Gospodnetic, Mosbach, Rauhut and Hagen [1] defined fundamental requirements for surface inspection planning pipelines and gave an example of a semi-automated visual inspection planning pipeline. The presented pipeline uses the geometry of the object for the purpose of viewpoint placement.…”

Section: Related Workmentioning

confidence: 99%

“…Automated production further requires automated inspection of the products. Due to its high availability and applicability, visual inspection is a frequent inspection method [1]. Therefore, automated visual inspection, such as surface inspection, can be considered as an important part of quality assurance in the Industry 4.0 concept.…”

Section: Introductionmentioning

confidence: 99%

“…Even though the inspection process itself is automated, development of one such inspection system requires an expert approach. For that purpose, research efforts have been focused on development of automated inspection planning tools, which can be used as an aid to experts developing surface inspection systems [1], [2]. The main goal of such planning tools is to find the optimal setup of acquisition hardware (camera and illumination) that ensure complete surface coverage of the product.…”

Section: Introductionmentioning

confidence: 99%

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Image Synthesis Pipeline for Surface Inspection

Bosnar¹,

Saric²,

Dutta³

et al. 2020

Preprint

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Surface inspection development requires large amounts of image data representing the inspected product surface. The image data should contain both the ideal surface and the defective surface that can appear during production. Although image synthesis comes as a natural solution to this problem, its application is not straightforward in automated surface inspection environments. The reason for that is a lot of manual work that should be done for creating defects, simulating the inspection environment, and setting up the acquisition system for validation of simulation. To address these issues, we present a novel pipeline that automatizes surface defect creation, provides realistic rendering in a predefined inspection environment setup, and an acquisition system that enables comparison with the real images. The pipeline creates geometry-imprinted defects which combined with physically based rendering methods enable realistic light response for different light and camera positions during image synthesis. Finally, synthesized images can becompared with the real image taken in the same setup enabling verification. Also, synthesized images enable the visualization of visible surfaces and defects for a given inspection plan.

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