With the advent of Industry 4.0, machine vision is playing an increasingly important role in the field of intelligent manufacturing. In order to enable more users to acquire basic knowledge about machine vision, including how machine vision technology works and why it is the right choice for achieving process automation and quality improvement, this article summarizes introductory knowledge about machine vision.
Machine vision is a scientific and technological field widely used in industrial fields such as manufacturing and inspection to ensure product quality, control production processes, and perceive the environment. A machine vision system converts the captured target into an image signal, which is then transmitted to a dedicated image processing system. Based on pixel distribution and information such as brightness and color, this signal is converted into a digital signal. The image system performs various calculations on these signals to extract the target's features, and then controls the actions of on-site equipment based on the judgment results.
Advantages of machine vision: Machine vision systems are highly efficient and automated, achieving high resolution accuracy and speed. Machine vision systems operate without contact with the object being inspected, making them safe and reliable. The main differences between manual inspection and automated machine vision inspection are:
To better understand machine vision, we will introduce several case studies in specific applications below.
Let's take the filling level detection system used in a brewery as an example to illustrate:
As each beer bottle moves past the detection sensor, the sensor triggers the vision system to emit a strobe light and capture an image of the bottle. After acquiring the image and saving it to memory, the vision software processes or analyzes the image and issues a pass/fail response based on the actual fill level of the bottle. If the vision system detects that a beer bottle is not fully filled (i.e., it fails the detection), it will signal a deflector to remove the bottle from the production line. Operators can view the rejected bottles and ongoing process statistics on a display screen. With the advent of Industry 4.0, machine vision plays an increasingly important role in intelligent manufacturing. To help more users acquire basic knowledge about machine vision, including how machine vision technology works and why it is the right choice for achieving process automation and quality improvement, this introductory learning material on machine vision has been prepared for you.
Machine vision is a scientific and technological field widely used in industrial fields such as manufacturing and inspection to ensure product quality, control production processes, and perceive the environment. A machine vision system converts the captured target into an image signal, which is then transmitted to a dedicated image processing system. Based on pixel distribution and information such as brightness and color, this signal is converted into a digital signal. The image system performs various calculations on these signals to extract the target's features, and then controls the actions of on-site equipment based on the judgment results.
Advantages of machine vision: Machine vision systems are highly efficient and automated, achieving high resolution accuracy and speed. Machine vision systems operate without contact with the object being inspected, making them safe and reliable. The main differences between manual inspection and automated machine vision inspection are:
To better understand machine vision, we will introduce several case studies in specific applications below.
Let's take the filling level detection system used in a brewery as an example to illustrate:
As each beer bottle moves past the detection sensor, the sensor triggers the vision system to emit a strobe light and capture an image of the bottle. After acquiring the image and saving it to memory, the vision software processes or analyzes the image and issues a pass/fail response based on the actual fill level of the bottle. If the vision system detects that a beer bottle is not fully filled (i.e., it fails the detection), it will signal a deflector to remove the bottle from the production line. The operator can view the rejected bottles and ongoing process statistics on the display screen.
