Deep Learning Lessons
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Deep Learning Lessons

By Alex Reznichenko, Machine Vision Specialist, Edgewater Automation

Alex Reznichenko, Machine Vision Specialist, Edgewater Automation

Deep learning has been getting a lot of attention in the machine vision industry. The push into manufacturing is aggressive, but often the results have been less than advertised. While deep learning is a truly revolutionary technology, it is vital to understand that deep learning, as a powerful tool, is not the solution to all vision applications.

Deep learning and How It Works

Deep learning, also known as deep structured learning, is part of a broader subject of machine learning method. There are three types of layers of neurons in a neural network: the Input Layer, the Hidden Layer(s), and the Output Layer. The input layer receives data. In our case, it would be a digital image. It uses hidden layers that perform mathematical computation based on the digital image information provided. The “Deep” in Deep Learning refers to having more than one hidden layer. Each connection between the neurons is associated with a calculation that dictates the importance of the input value. Once a set of input data has passed through all the layers of the neural network, it returns the output data through the output layer. In the most simplistic terms, deep learning is a highly dynamic algorithm. The system is trained by presetting “good” and “bad” digital images (a.k.a. labeled images). In some cases, teaching only the “good” image, the system can find defects on parts. It is an innovative and advanced technology for the machine vision industry.

Outstanding Areas of Deep Learning

Deep Learning technology has presented some great solutions that show off its potential. First, it is highly adaptive. Deep learning can be adapted to a new part sample relatively easy and can handle defect variations for applications that require an array of acceptable deviations. For example, detecting and analyzing scratch defects on a plastic door panel. A deep learning system can detect scratches dynamically on the entire board. Next, deep learning can solve complex inspections. The once impossible or difficult review, classification, or location with classic rules-based algorithms are now conceivable with in-depth knowledge. Self-learning algorithms make it possible to locate features or parts and perform quality control checks on kits and packages.

A great example is components on circuit boards that are wrong or missing. The self-learning ability in deep learning software essentially learns as children do, by example. Instead of employing classic task-based inspection algorithms, deep learning systems use a neural network to examine a library of images labeled by humans. The systems use this classification method to learn the subtle distinctions between defects that may present in a variety of ways. Deep learning can be efficient at delivering high-quality results. Once trained correctly, a deep learning “brain” can perform thousands of repetitive, routine tasks within a shorter period than it would take a human being. The quality of its work never diminishes: however, the training data represent the problem you are trying to solve to achieve the level of quality required. Where deep learning systems shine is in OCR. The ability to read characters in various light scenarios, different part presentation, and deferent materials is impressive.

Challenge Areas of Deep Learning

Deep Learning technology brings with it some challenges that must be considered. First, deep learning is a black box. One of the most discussed limitations of deep learning is the fact that we do not know how a neural network arrives at a particular solution. It is impossible to look inside to see how it comes. The hidden layers form an intricate web where inputs travel from one level to the next until an overall output is produced. In machine vision, however, it is imperative to know why a particular part failed. Applications that require specific feature inspections and deliberate pass or fail criteria find it challenging to implement a robust deep learning system. The only output the system provides is to pass or fail based on the internal calculations of the algorithm. This makes it challenging to validate parts for quality. Also, deep learning requires a large amount of data. Some companies claim it only takes a few images, but the reality is it takes massive amounts of images to train the system thoroughly. The reason is that the task of a deep learning algorithm is two-fold. First, it needs to learn the domain before it can solve the problem. When training begins, the algorithm starts from scratch. To learn about a given area, the algorithm needs a considerable number of parameters to tune and learn. Just like a human brain needs a lot of experiences to learn and understand the world around it. If you only have a limited amount of examples, deep learning is unlikely to outperform traditional vision applications. Another challenge is to overfitting the model. Overfitting refers to an algorithm that models the training data too well or, in other words, one that over trains the model. Overfitting happens when the algorithm learns the detail and noise in the training data to the extent that negatively impacts the performance of the model in real-life scenarios. The accuracy of the system can reach a limit of only 82-85%.

"Deep Learning technology has presented some great solutions that show off its potential"

Deep learning is high and revolutionary technology. It has made things possible that were once difficult or even impossible. In cases of part or component location, defect analysis and object classification, deep learning can be the perfect tool even under poor lighting and noisy background. However, it is only a tool and not the solution to all vision applications. Deep learning can be a great solution or a costly gadget. Just like any other tool, one must choose the right tool for the job at hand.

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