Visual inspection is essential for mass production and other industry practices. Deep learning can help not only ease the burden of visual inspection but can do it even more efficiently than a human. We should, however, consider deep learning’s limitations.

We’ll look at various methods of development and see their strengths and weaknesses. Along the way, we’ll see how deep learning can benefit the visual inspection field.

How Does AI Accomplish Visual Inspection?

Consider the job of an inspector on a factory line. They need to quickly distinguish the difference between good and bad items as they pass through inspection. The inspector observes the item, considers its features, and then makes a judgment. For a computer, the process is the same. A camera sends video data to the machine which then processes the information and makes a judgment based on its deep learning model. However, the machine must be trained to understand the difference between a good or bad product by recognizing defects.

Deep Learning Requires Data

Deep learning model development takes a lot of data, mostly video footage. As quality of the footage increases, so does the accuracy of the model.

There are various methods of training a deep learning model for visual inspection roles with data. You can use preexisting video provided by a client, use open-source video, or gather new data. It’s important to be sure that the data used is relevant to the task and is checked for anything that might hinder the deep learning training process.

For example, if you were training a computer to recognize defects in car suspensions, it would be inappropriate to have data featuring components of the car’s radio as it’s unrelated to the task.

Which Method Should You Choose?

Consider the task’s complexity, how long it should take, and your budget. How precise does the inspection process need to be? How many items need to be inspected per unit of time?

Third-party Deep Learning Model Development Services

This method is ideal if your inspection case aligns with that of another industry. If the model already exists, there’s no need to reinvent the wheel by investing time and money into developing a brand-new deep learning model. Google’s Cloud ML Engine and Amazon ML both provide these services.

However, the problem with using these services is that they come with a sacrifice of flexibility. If your inspection requirements are too far off from their model, then the visual inspection process may not be accurate enough.

Pre-trained Models

Just as using the templates provided by Google, Amazon, or another firm can fit your use case, a pre-trained deep learning model that aligns with your visual inspection requirements may be able to get the job done. This again eliminates the need to train a new model.

There’s still a chance that the pre-trained model may not be fully up-to-date or perfectly reliable. However, depending on your needs, using a pre-existing model can significantly reduce costs and time, especially if the model was trained with large datasets to enable flexibility.

Developing a New Deep Learning Model

If your task is particularly complex or unique, it’s likely that an existing model isn’t compatible, and a new model must be trained. This provides an accurate and efficient system at the cost of time and effort to develop.

Training Visual Inspection Models

Data scientists have a few different methods that they use to develop visual inspection models called deep learning algorithms. For this role, they are concerned with vision-based methods such as image classification, object detection, and instance segmentation.

The method used is dependent on the requirements of the task. For example, if your system is meant to detect defects on walls, you’ll need a large data set. Imagine all the different kinds of problems with walls. Perhaps the paint is scuffed, or there might be water damage or peeling. In this case, training a new model using instance segmentation would be appropriate.

This also applies to pharmaceuticals. Let’s say that your visual inspection system needs to be able to tell the difference between air bubbles from particles in viscous parental solutions. Bubbles are the only defect that the computer needs to identify, so a large data set isn’t needed, and using a pre-existing model may be the best choice.

Other Important Factors to Consider

Aside from these factors, it’s important to consider the limitations of available software and hardware. Visual inspection software is based on a combination of web data transmission methods and Python frameworks for neural network processing.

Also, consider storage limitations. Video training files are large, so cloud storage is an optimal solution. But you also may use a serverless architecture and local server. The evolution of deep learning models is dynamic. It means that your model can be improved over time. Once your model is in use, expect that its accuracy will rise over time as it gathers new data. The longer that it runs, the smarter it gets.

 

Original post: https://www.thomasnet.com/insights/considering-ai-for-visual-inspections-here-s-how-to-develop-the-right-deep-learning-model-for-your-operation/