At Gemmo, we offer AI manufacturing solutions, including object detection and object tracking for anomaly detection. Gem...
At Gemmo, we offer AI manufacturing solutions, including object detection and object tracking for anomaly detection. Gemmo can help you today to set up a fault detection system with computer-vision solutions, which will boost your company’s quality control, performance, and safety. In this article, we will discuss generative deep learning image anomaly detection, and show you examples in action. Be thinking on how your business can benefit from AI.
Generative Deep Learning is becoming increasingly useful to supply chain companies with the application of image anomaly detection. What is generative deep learning? AI algorithms are created and used to perform repetitive tasks. Over time, these deep learning AI algorithms simultaneously examine varying datasets while processing end solutions. Essentially, these AI algorithms learn to flag anomalies at the most acute level. The process is not a static technical code, instead, it is a generative deep learning algorithm. A great example of this is on assembly manufacturing lines. Implementing generative deep learning allows tasks to be performed with near-zero efficiency loss. Generative deep learning can handle even the rarest of anomaly detection cases.
What is anomaly detection? “Anything that deviates from an established baseline pattern is considered an anomaly” (Dynatrace). For example, an apple might ‘pass’ or ‘fail’ a baseline test based on its appearance. Thus, some things to keep in mind are that challenges of AI-based anomaly detection can include data, definition, and generalisation. Anomalies within datasets can be rare. Therefore, the AI needs to be fine-tuned. The definition of anomaly needs to be defined so that the right data is being captured. Another question to ask yourself is, what should the AI do when it encounters situations that it has never seen before?
Generative Deep Learning for Image Anomaly Detection can be used to improve efficiency by increasing productivity, effectiveness (Machines have no loss of attention, but more attention with cameras available 24/7.), safety of the workplace and workers, and to provide new opportunities for workers to be assigned to more complex and profitable tasks.
Image anomaly detection is proactive instead of reactive.
It’s dynamic. It’s real-time.
Intrinsically, an AI algorithm learns the anomalies from a visual perspective. This is generative deep learning for image anomaly detection in a nutshell. Most importantly, generative deep learning is an architectural base setup on cameras. Generative deep learning and image anomaly detection are most known and most used in manufacturing production lines. With the use of image anomaly detection, generative deep learning can quickly detect if a product, such as an apple, is “pass” or “fail” based on its appearance.
This is where the generative deep learning part emerges. These AI algorithms come to know which anomalies are important. They can be trained to alert the system or staff, and even to categorise and sort lower anomaly issues. As we all know very well, not every issue that arises is critical, nor are all issues rated the same level of priority. Thus, image anomaly detection can classify each anomaly as high impact, low impact, high importance, low importance, etc.
Generative deep learning focuses on the problem that really matters.
Types of Generative Deep Learning techniques include AutoEncoders, GANs, and Contrastive Learning.
We use Variational autoencoder (VAE) here at Sparkd. This can be used to detect defects. A great example of using VAE in generative deep learning image anomaly detection is for bottles or bolts. VAE uses an input image. In this case (see image below), the input image is the groove part of the bottle top, where the lid screws onto the bottle. From looking at images at the top of the bottle grooves, AI can easily determine which one has a defect. The output image is what each individual bottle looks like. So, when multiple bottles are together on an assembly line, the cameras take a quick snapshot image. Then, the AI algorithm can easily detect anomalies. After this, it creates a residual image to show the staff where exactly the anomaly is on the bottle.
Another type of deep learning technique is Generative Adversarial Network (GAN). (This explanation will be just surface-level only.) GANs can be unsupervised or supervised learning models. If your company has a lot of data for instance and you don’t know how to process it all or categorise it, an unsupervised model is a great starting place. An unsupervised GAN can learn a task and automatically discover patterns or similarities within datasets. All that is required is that you feed the GAN with input data. It will generate possible outcomes, patterns, or categorisations based on that data in order to show you different end result possibilities. This is extremely useful when you have input variables, but you are unsure what the output variables should be or what you want them to be yet because it is hard to visualise with the complexities presented from the input data itself. Thus, GANs construct models from input data to output by extracting and summarising the input data, which then creates an output pattern or categorisation. (Again, there are many variations and more actions that GANs can accomplish.)
While we only provided two examples here, there are a multitude of different techniques and generative deep learning models and types that can be used in anomaly detection.
We encourage your company to discard static modifications or software that cannot detect all anomalies and issues real-time. Instead, think of ways to implement generative deep learning and image anomaly detection into your company’s manufacturing tasks. Generative deep learning models can be trained and taught to detect anomalies at the most minute level. Unlike other softwares or coding, generative deep learning is dynamic and uses predictive analysis to detect anomalies. Implement image anomaly detection today to avoid false positives in your quality control processes.
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Author: Samantha Sink