Let’s take a look at the error control methods of machine vision inspection systems.
The emergence of machine vision systems originates from the need to replace tedious manual labor. Machine vision automation equipment can tirelessly perform repetitive tasks, and in hazardous working environments that are unsuitable for human operation, or in cases where human vision cannot meet requirements, machine vision can serve as a substitute.
Due to its outstanding advantages of non-contact operation, high speed, and strong flexibility, machine vision holds significant application prospects in modern manufacturing. Introducing machine vision into industrial inspection enables rapid measurement of the two-dimensional or three-dimensional position and size of objects, thereby greatly improving production efficiency and inspection accuracy.
In machine vision inspection systems, measurement errors generally include three components: mechanical errors, calibration errors, and analytical errors. Mechanical error arises from the hardware of the electromechanical execution part of the system. For example, if a workpiece cannot be fully measured within a single image, it may need to be repositioned to capture multiple images. In this case, the motion accuracy of the electromechanical system will significantly affect measurement precision.
This type of error can be calculated based on the motion accuracy, and it accumulates during multi-step movements. Therefore, the number of motion steps in the measurement process should be minimized. When building an inspection system, the system’s error tolerance should be reasonably allocated according to actual inspection conditions. The main methods include:
1.Simplifying the motion steps of the electromechanical system and improving the hardware precision of the system;
2.Using high-precision calibration algorithms and calibration templates;
3.Enhancing image quality and adopting the smallest possible object-to-image ratio.
The application of machine vision in China originated in the 1980s through technology introduction, with the semiconductor and electronics industries being among the earliest adopters. As China has gradually become a global manufacturing hub, it has also become one of the most active regions in the development of machine vision. Its application scope now covers a wide range of sectors across the national economy, including industry, agriculture, medicine, military, aerospace, meteorology, astronomy, public security, transportation, safety, and scientific research.
