Suppression de la distorsion géométrique de l’objectif grâce au traitement d’image avancé
Comprendre la distorsion de l’objectif dans la vision industrielle
La distorsion de l’objectif dans la vision industrielle signifie que les informations de l’image sont mal placées géométriquement par rapport aux formes idéales. Il existe deux principaux types de distorsions de l’objectif : radiale et tangentielle. Et les distorsions radiales font généralement référence aux distorsions en barillet, coussinet et moustache.
La distorsion en barillet est généralement associée à un objectif grand angle (distance < de 50 mm ; structure avant de l’ouverture) tandis que la distorsion en coussinet est courante avec un téléobjectif (distance> de 50 mm, structure arrière de l’ouverture). La distorsion en moustache est un mélange des deux types et moins courante.
La correction de la distorsion de l’objectif est nécessaire pour les applications de vision industrielle où des résultats précis et fiables sont cruciaux. Les images déformées peuvent entraîner des erreurs de mesure et de mesure, affecter les performances des algorithmes d’apprentissage automatique et affecter la fiabilité globale du système.
What was the problem?
Lens distortion can be limited or removed through hardware or software approaches. Rectilinear lenses are designed to minimize geometric distortions and the advantage of this hardware approach is simplicity. Applying correction directly at the time of image capture means no additional post-processing. However, if your application requires more flexibility in lens choices and fine-tuning different types of distortions, the software option is more suitable.
The key challenges in using the software approach to remove geometric distortions are: first, the task is computationally intensive because it is typically performed on pixel level; second, developing algorithms from scratch is very time-consuming, especially if you are not familiar with the intricacies of lens distortion.
The solution
In this use case, you will learn Basler’s solution to compensate barrel distortion for wide FOV application as an example. The solution can be fine-tuned depending on different project requirements.
As depicted in the system diagram below, the frame grabber plays a crucial role in collecting raw image data, processing it into different versions, and simultaneously transmitting it to the PC DMA. This comprehensive process prepares the system for the ultimate analysis, interpretation, or application of the refined image data.
Algorithms are often combined in various ways to create comprehensive image processing pipelines tailored to the specific application requirements. For instance, besides correcting distortions, we concurrently apply histogram stretching and blob analysis algorithms. This ensures that the resulting image is well-refined, highlighting specific features for easy interpretation by the computer.
Your benefits
Real-time processing and low latency: the frame grabber excels at concurrently managing image transfer and complex image processing.
Flexibility and customization: tailor the solution to your application swiftly and seamlessly.
Faster time-to-market: streamline your project implementation process and bypass unforeseen technical hurdles.
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