Deep Learning with Cognex ViDi Suite

Your Contact Person

If you have any questions or are interested in consulting, please feel free contact me.

Markus Schatzl senswork
Markus Schatzl

+49 (0)89 215 298 46 0

senswork GmbH
Innovation Lab
Friedenstraße 18
81671 München


Praline Box Inspection
Location & Position Recognition and Completeness Confirmation using Deep Learning
Quality Assurance for First Aid Kits
Location Detection and Completeness Confirmation using Deep Learning
Reading a Raised Inscription on Smartphone Cases
Robust Reading of the Label using Deep Learning based OCR
Reading the „best before“ date on beverage bottles
Font Reading for Transparent Packaging using Deep Learning
Reading the „best before“ date on bottoms of bottles
Font Reading for Transparent Packaging using Deep Learning
Error Detection for Tortillas
Optical Inspection using Deep Learning
Quality assurance for LED curcuit boards
Error detection with the help of Deep Learning
Inspection of Glass Vials
Defect Detection on Reflective Surfaces using Deep Learning
Air Filter Fault Detection
Examination of Surface Damage using Deep Learning
Spark Plug Inspection
Location Detection and Completeness Confirmation using Deep Learning
PCB assembly verification of smartphone boards
Component Placement Inspection using Deep Learning
Recognition of Fonts for Ring Clamps
Optical Inspection of Surfaces with Inhomogeneous Topology
Surface Inspection of Cylindrical Aluminum Bodies
Examination of Surface Damage Through 360° Endoscopic Inspection

Deep Learning and senswork

senswork has many years of experience in the industrial image processing sector, due to several employees who deal with deep learning tasks in this industry, which has existed for more than 30 years, both in management and developer positions.We know the whole breadth of image processing and thus also the depths, trends and hypes.

Classification methods that have a certain overlap with the service promises of DL / ML have been used in industrial image processing for 20 years.

Standardized toolkits and the enormous increase in performance in the hardware area are indicative of the strong surge in recent years. In combination, this enables things with a scope of complexity whose practical implementation was not conceivable ten years ago. We have been accompanying this development for many years and research on it in our Innovation Lab.

Deep learning and machine learning (DL / ML) are methods from the research area of ​​artificial intelligence (AI). AI is originally a domain from computer science, the aim of which is to model software in such a way that it is capable of learning to a certain extent and, in terms of its interactions, indistinguishable from human actions.

Use of DL / ML at senswork

For the use of DL / ML processes in image processing, senswork offers comprehensive support throughout the entire planning and integration process.

When selecting a suitable toolkit, we are independent and orientate ourselves towards the optimal solution - this can consist of using our own deep learning software as well as the libraries of well-known manufacturers. Of course, we also support our customers with advice, training and implementation as a single service.

A non-binding consultation is essential for inquiries about new image processing systems. From our experience, it is usually easy to assess whether the requirements can be solved with conventional methods, deep learning or machine learning, or possibly not at all.

With DL / ML systems, we always carry out a feasibility analysis in advance. For such studies, image data of the process to be checked are necessary, which are either already available at the customer or which we record on site with an image processing system to be designed.


The basis of the implementation of DL / ML applications at senswork is that we carry out feasibility studies free of charge, but the customer undertakes to commission them if the study proves successful implementation.

In 2D applications, deep learning / machine learning is particularly strong for tasks on diffuse, inhomogeneous surfaces in which the error features have a wide range. These include for example:

  • Die casting technology: Detection of cracks and voids on rough surfaces
  • Surface inspection: grooves, grinding marks, inhomogeneities, planarity defects
  • Particle detection: detection and classification of contaminants
In the area of ​​3D applications, successful results can be achieved, especially with test objects with irregular shapes or naturally caused shape variations:
  • Seal seam inspection: quality and tightness of weld seams
  • Classification: Assessment of plants and natural products
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