Introduction: Machine vision has been developing in China for over a decade. The past ten years have been the fastest-growing decade for the machine vision industry in the Chinese market. After a period of popularization and promotion, machine vision has gradually become familiar to a wide range of customers, and its application scope has gradually expanded. Large-scale applications have gradually expanded from the initial electronics and pharmaceutical industries to major fields such as packaging and printing.
The machine vision market is developing and machine vision technology is advancing. While continuously meeting the development needs of customers, the satisfaction of the most basic needs cannot be ignored.
my country's technological level has always been in a stage of continuous development. As a product of technological development, machine vision technology is also constantly being optimized and upgraded to better adapt to industry needs. Looking at the industry's development, the domestic machine vision market faces both opportunities and challenges, making technological upgrades particularly necessary.
In the field of industrial production, industrial robots rely heavily on machine vision to inspect products. The sensitivity of vision directly affects the inspection speed and quality of products. Therefore, it is particularly important to design a high-quality vision product. During the design process, designers will face many challenges such as visual positioning, measurement, inspection and recognition.
I. Lighting Stability Industrial vision applications are generally divided into four categories: positioning, measurement, detection, and recognition. Among them, measurement has the highest requirements for lighting stability. This is because if the lighting changes by 10-20%, the measurement result may deviate by 1-2 pixels. This is not a software problem, but a change in lighting that causes the position of the upper edge of the image to change. Even the most advanced software cannot solve this problem. It is necessary to eliminate the interference of ambient light from the perspective of system design, and at the same time ensure the luminous stability of the active lighting source.
Of course, improving camera resolution is also a way to increase accuracy and resist environmental interference. For example, if the previous camera corresponded to an object space size of 10µm per pixel, then by increasing the resolution, it becomes 5µm per pixel. The accuracy can be considered to have been improved by approximately 100%, which naturally increases the resistance to environmental interference.
2. Inconsistency in workpiece position. Generally, for measurement projects, whether offline or online, as long as it is a fully automated testing device, the first step is to find the target object to be measured.
Each time the target object appears in the field of view, it is necessary to know exactly where the target object is. Even if you use some mechanical fixtures, it is not possible to guarantee that the target object will appear in the same position every time with high precision. This requires the use of positioning function. If the positioning is not accurate, the position of the measuring tool may be inaccurate, and the measurement results may sometimes have a large deviation.
III. Calibration generally requires the following calibrations during high-precision measurements: First, optical distortion calibration (which is generally necessary if you are not using a software lens); second, projection distortion calibration, which is the correction of image distortion due to your installation position error; and third, object space calibration, which is to specifically calculate the size of the object space corresponding to each pixel.
However, current calibration algorithms are all based on planar calibration. If the physical object to be measured is not planar, calibration will require some special algorithms to handle it, which the usual calibration algorithms cannot solve.
In addition, some calibrations require special calibration methods because it is inconvenient to use a calibration board. Therefore, calibration cannot necessarily be solved by the existing calibration algorithms in the software.
Fourth, the speed of an object's motion: If the object being measured is not stationary but in motion, then the impact of motion blur on image accuracy must be considered (blurred pixels = object's speed * camera exposure time), which is not something that software can solve.
V. Measurement Accuracy of the Software In measurement applications, the accuracy of the software can only be considered at 1/2 to 1/4 of a pixel, preferably 1/2, and cannot reach 1/10 to 1/30 of a pixel as in positioning applications, because the software can extract very few feature points from the image in measurement applications.
The motion speed and measurement accuracy of machine vision play an important role in the entire product. The speed of motion is inversely proportional to the detection capability. The faster the motion, the worse the detection quality. Therefore, it is important to improve motion accuracy and detection details.
Furthermore, machine vision facilitates information integration and is a fundamental technology for computer-integrated manufacturing. Five main reasons for using machine vision systems:
Repeatability – A machine can perform an inspection task repeatedly in the same way without getting tired. In contrast, the human eye makes subtle differences each time it inspects a product, even if the product is exactly the same.
Precision – Due to the physical limitations of the human eye, machines have a clear advantage in precision. Even when the human eye relies on a magnifying glass or microscope to inspect products, machines are still more precise because their accuracy can reach one-thousandth of an inch.
Speed-up machines can inspect products much faster. Especially when inspecting high-speed moving objects, such as on a production line, these machines can improve production efficiency.
Objectivity – Human eye inspection has a fatal flaw: subjectivity caused by emotions. The inspection results will change depending on the worker's mood, while the machine has no emotions, so the inspection results are naturally very objective and reliable.
Cost – Because machines are faster than humans, one automated inspection machine can perform the work of several people. Moreover, machines do not need to stop, do not get sick, and can work continuously, thus greatly improving production efficiency.
Machine vision systems are characterized by improved production flexibility and automation. They are commonly used to replace human vision in hazardous working environments where manual labor is unsuitable or where human vision alone is insufficient. Furthermore, in large-scale industrial production, manual inspection of product quality is inefficient and lacks precision; machine vision inspection methods can significantly improve production efficiency and automation.
