Machine vision systems comprise lenses, lighting systems, camera systems, and image processing systems. For each application, factors such as system operating speed and image processing speed, the use of color or monochrome cameras, the size of the target being detected or whether it has defects, field of view, resolution, and contrast must be considered.
Machine vision systems are finding increasingly wider applications, including in industries such as manufacturing, transportation, healthcare, agriculture, defense, finance, and even sports and entertainment. In short, they have permeated every aspect of our lives, production, and work.
Functionally, a typical machine vision system can be divided into three parts: image acquisition, image processing, and motion control.
I. Composition of Machine Vision System
The design of a vision system is divided into two main parts: software design and hardware design.
(I) Software Design of Vision Systems
The software design of a vision system is a complex subject. It requires consideration of not only the optimization of the program design, but also the effectiveness of the algorithm and its feasibility. Potential problems must also be taken into account during the software design process.
After the software design of the vision system is completed, its robustness must be tested and improved to adapt to complex external environments.
(II) Hardware Design of the Vision System
The hardware of a vision system mainly consists of a lens, camera, image acquisition card, input/output unit, and control device.
The quality of a vision system depends on the camera's pixel count, the quality of the hardware, and, more importantly, the coordination and proper use of its various components.
II. Working Principle
Machine vision hardware mainly consists of two parts: image acquisition and vision processing. Image acquisition comprises a vision sensor, a lighting system, an analog-to-digital converter, and a frame buffer. Robot vision acquires two-dimensional images of the environment through vision sensors, analyzes and interprets them through a vision processor, and then converts them into symbols, enabling the robot to recognize objects and determine their locations.
