What you need to know to learn machine vision

       With the advent of the Industry 4.0 era, a simple machine vision system includes a visual imaging part, an image processing part and a motion control part. The editor will explain it to you below:

1.Visual imaging part

Visual imaging includes several typical components: light source, lens, and industrial camera.

Both light sources and lenses require us to master optical knowledge. Different lighting methods can allow the camera to produce completely different images of objects; and the different choices of lens magnification, focal length, field of view, etc. directly determine the fidelity of the imaging. For a machine vision engineer, mastering how to choose lenses, how to choose light sources, and how to determine lighting methods are the most basic skills.

Industrial cameras require us to master photoelectric knowledge, master the differences between camera sensors, and master basic knowledge of image imaging such as sharpness, dynamic range, field of view, etc., so that we can choose the right camera according to needs and scenarios, and master these as quickly as possible The method of knowledge is to buy an entry-level SLR to study the relationship between these imaging parameters and imaging.

2. Image processing part

We generally understand that image processing is performed on PC machines. In fact, in the industrial field, most industrial computers are used because of their stability and cost advantages.

In recent years, embedded hardware has also been booming. Many factories can use open source hardware such as Raspberry Pi to realize small needs, such as controlling the switches and status monitoring of hundreds of dashboards.

For beginners, you can give priority to mastering the PC platform. The development of the X86 platform can be extended to the embedded platform after becoming familiar with it.

In the software part, most application layers are implemented using C#, .net, QT, and C++, so mastering one of these programming languages is a must; at the image algorithm level, typical open source algorithms include opencv, and commercial ones include Halcon, VisionPro, etc., it is recommended to start with Halcon; if you want to go further at the algorithm level, you can study machine learning, which may be the main direction in the future.

For the theoretical aspect, it is more about mastering the basic concepts of image processing. This book "Machine Vision Algorithms and Applications" is worth reading.

3. Motion control part

Typical motion control cards such as Gogo can be studied. More advanced PLCs can also be played. The difficulty in this part lies in the accuracy correction, because many scenarios and needs have very high accuracy requirements.

In addition to the above three points, the ability to build an overall solution is crucial, because the solution needs to string together these parts and be able to connect with real scenarios to meet actual production automation needs.