Automated Visual Inspection with Artificial Intelligence (AI)
Detecting defects before they become a risk.
At WILCO, we rely on state-of-the-art technologies to take the inspection of pharmaceutical products to the next level. By integrating artificial intelligence (AI) and neural networks, we optimize the precision and reliability of automated visual inspection systems - even for difficult-to-detect defects.
AI, machine learning and neural networks - simply explained
Artificial intelligence (AI) enables machines to solve complex tasks independently. Machine learning (ML), in which algorithms recognize patterns based on large amounts of data, is particularly powerful. Advanced methods include neural networks that work in the same way as the human brain. They analyze image data with particular precision and adapt flexibly to different types of defects - ideal for automated visual inspections.
At WILCO, we use neural networks to reliably detect even the most subtle quality deviations - more efficiently and precisely than with classic, rule-based systems. In this way, we improve defect detection, reduce manual intervention and ensure the highest product quality.
Different AI Methods suitable for Automated Visual Inspection
In order to fully exploit the potential of AI in error detection, various machine learning approaches come into play, each of which is particularly suitable for a specific task.
Supervised Learning: Teaching AI to Recognize Defects
In this approach, humans manually label a large number of defect images, helping the AI model learn what to detect. Through extensive training, the model learns to recognize similar defects with high precision. This method is effective for well-defined defects, but requires a significant amount of labeled data. As the amount of high-quality training data increases, the recognition accuracy improves. It is important to avoid overfitting so that the model not only fits the training data, but also works accurately on unknown data.
Anomaly Detection: Identifying Deviations from the Norm
Unlike supervised learning, anomaly detection focuses on distinguishing between normal and deviant samples. It is particularly useful when rare or previously unknown error patterns occur. The AI is trained using defect-free products and learns to recognize anything that deviates from this standard.
This approach does not require annotated data, but is trained exclusively with defect-free data. However, this can be challenging in regulated environments—especially when accurate defect classification is required and each defect must be specifically assigned.
Why AI in visual inspection?
In pharmaceutical production, the smallest deviations from quality standards are crucial. Classic, rule-based systems often reach their limits here. AI, on the other hand, can detect and visualize deviations from the norm.
- Higher detection rate for challenging defects
- Fewer false rejects thanks to robust defect classification
- Easy maintenance thanks to customizable models
- Flexibility in the face of product and packaging variability
AI shifts the boundary between good and bad - in favor of your product safety
How AI integration works at WILCO - the 4-step process
The integration of artificial intelligence into automated visual inspection follows a structured four-step process.
1. Data acquisition & image pre-processing
High-resolution images are captured under stable conditions - the key to reliable training data.
2. Data labeling
Data labeling ensures that images are categorized correctly so that the AI model can reliably detect different types of defects.
3. Model training & evaluation
The model learns from the labeled data and tests it for accuracy and reliability.
4. Integration & real-time monitoring
The trained model is seamlessly integrated into the inspection machine and monitored.
As a machine manufacturer, our core competence lies in building high-performance inspection systems. Therefore, we focus on two crucial steps in this process:
- Data acquisition - capturing high-quality images of the products during the inspection.
- Integration - integrating the trained AI model into our machines for real-time defect detection.
The intermediate steps - model training and evaluation - are usually performed by specialized AI developers. We offer a flexible deployment framework that enables our customers to carry out these intermediate steps with the tools of their choice. Our AI specialists are happy to support you in this.
Our framework allows new models and training approaches to be tested in order to find the best possible solution for your product - our own AI experts are always available to support you in the development process. Once a reliable AI model has been trained, we ensure its seamless integration into our machines to further improve inspection performance.
AI integration at WILCO simply explained
Challenges when implementing AI in inspection machines: Revalidation
As soon as AI is integrated into an inspection machine, the system must be validated to ensure compliance with legal standards. However, if AI is only used to replace certain functions and not the entire inspection process, only partial revalidation is required. This reduces the validation effort and improves the efficiency of the implementation.
Upgrading certain machines with AI is possible on the hardware and software side, but a revalidation process is required.
What makes WILCO special
Open architecture: Our platform is open for both commercial and open source models
Flexible AI integration: Customers choose their own tools for training and labeling
AI-ready hardware: Our camera systems and image processing are optimally prepared
Your entry into AI with WILCO
Our visual inspection solutions are designed to integrate AI models smoothly so that you can benefit from the advantages of AI-supported visual inspection with minimal effort. - We are there to advise you from the initial idea through to successful implementation and support you every step of the way.
Do you have questions about AI integration?
We are happy to advise you personally.
