Top 10 Deep Learning application types in industrial vision systems
Deep Learning technology in Machine Vision
Deep Learning is a new reliable solution for machine vision problems that could not have been solved before. Last time I wrote about untypical vision solutions, so this time I will focus on the common industrial deep learning applications. In this article I will show you solution types for automotive, PCB and food industry, but, in my opinion, deep learning tools can be used in almost every manufacture.
Deep Learning vision application examples
1. Anomaly detection in food inspection
Food industry is very specific and problematic for traditional computer vision algorithms, because each product has some acceptable differences. That’s why Deep Learning is very useful in food industry. Algorithms like anomaly detection are able to learn how good product looks and find any anomalies.
Deep Learnnig application for detecting anomalies on cookies. Source
2. OCR on metal parts
The most challenging OCR (optical character recognition) applications like laser etched codes on electronic components, low contrast characters, embossed characters on injection molded products and dot peens codes on metal parts are often not feasible using traditional machine vision methods. Deep Learning algorithms are really efective in those examples. They are able to extract text from background even in very difficult and changing conditions.
Deep Learning OCR on metal parts. Source
3. Assembly verification in automotive
Vision assembly verification in automotive industry could be a topic for a separate article. Detecting and localizing parts sometimes can be challenging, but in most cases feasible with traditional machine vision. So why Deep Learning is so popular for those applications? Usually there are many different types of the same product, in different color variants, with many variants of buttons, etc. As a result, creating these applications using Deep Learning is simply much faster.
Door assembly verification using Deep Learning methods.
4. Anomaly detection in textile inspection
The main problem in vision textile inspection is the pattern and fabric types complexity. There are several types of algorithms, which can be used to detect anomalies in textiles, but basically only Deep Learning gives us confidence that every type of defect will be found. Anomaly detection algorithms are very often used for this type of application because defect-free images are enough to train the Deep Learning model.
Deep Learning application for detecting anomalies in textiles. Image source.
5. Detecting scratches on metal parts
In automotive industry many components have complex shape and, what’s more, they are made of metal. This combination often makes inspection using traditional machine vision algorithms impossible. Deep Learning supervised tools / feature detecction tools are ideal for those applications. The best algorithms can detect even several pixel defects with almost 100% efficiency.
Production line in automotive. Image source.
6. Anomaly detection on plastic objects
As I mentioned before, Deep Learning algorithms are perfect for inspecting objects, which geometry is variable. Those objects are difficult to determine by parametric models. Plastic objects made in injection moulding process are such irregular objects.
Anomaly detection of plastic objects. Source
7. Part classification in automotive industry
Deep Learning application for classification objects from automotive. Source
8. Deep Learning applications for counting objects in food industry
Deep Learning algorithm concept for pasta counting.
9. Parts localization and segmentation on the PCB
Parts localization and segmentation on the PCB using Deep Learning algorithms. Source
10. Packing completeness in food industry
Deep Learning based application for checking completeness of box of chocolates. Source
Deep Learning based application for checking completeness of sushi boxes. Source