Introduction to Deep Learning

The whole industry has been very excited over the 2 key terms “deep learning” & “artificial intelligence”. There have been many new applications the industry uses this technology for, which is inclusive but not limited to: gaming, security & surveillance.

Some of the applications include face recognition, number plate reading, behaviour characterization, etc.  Deep learning has been used for the industrial applications for reading OCR & defect inspections. It has been proven that deep learning will be able to recognize a defect, even if the production parts have some different variation on the threshold or machining marks.

Deep learning has solved many problems with image classification, image reconstruction, object detection, et cetera. With a task like image processing, thousands of images are uploaded to a data set. The whole idea of deep learning is to be able to train sufficient sample sizes of good parts, and “marking” the defected area on NG parts. During the production, new group of NG parts can be marked & added to the existing database to improve the inspection capability.

Advantages of Deep Learning

      1. Self-Learning Capabilities
The layers in deep learning neural networks allow models to become smarter by learning complex features and performing repetitive tasks within a short period of time, as compared to the time taken by a human inspector.

This is due to deep learning algorithms having the ability to learn from its own errors and determine the accuracy of outputs from time to time.

      2. Computing Power and Data
One of the drawbacks in machine vision algorithms is the limitation of analysing unstructured data, and this is where deep learning becomes useful. Deep learning algorithms have the ability to make sense of data formats like texts, images and voices. This can help businesses obtain insights and identify every defect outside of the set tolerance, with no special expertise required.

      3. Generation Automation
Deep learning systems learn as a human would, but their learning curve is beyond human capabilities when it comes to analysing parts or product with defects, before providing a solution. In addition to this, deep learning algorithms can generate and perform complex tasks without additional human intervention. For businesses, it means that deep learning supports a faster and more reliable high-speed application on improving the speed of data throughput.