In the field of industrial production, robot inspection of products relies heavily on machine vision. 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. Stability of Lighting
Industrial vision applications are generally divided into four categories: positioning, measurement, detection, and recognition. Among these, measurement has the highest requirements for lighting stability. Even a 10-20% change in lighting can cause a deviation of 1-2 pixels in the measurement result. This isn't a software issue; it's a direct result of the lighting change, causing a shift in the position of the image's edges. Even the most sophisticated software cannot solve this problem. It requires a system design approach that eliminates interference from ambient light while ensuring the stability of the active illumination source. Of course, increasing the resolution of the hardware camera is also a way to improve accuracy and resist environmental interference. For example, if the previous camera corresponded to an object space size of 10µm per pixel, increasing the resolution would reduce it to 5µm per pixel, effectively doubling the accuracy. However, this naturally increases susceptibility to environmental interference.
II. Inconsistency in workpiece position
In general measurement projects, whether offline or online, the first step with fully automated equipment is to locate the target object. Each time the target object appears in the field of view, its exact location must be known. Even with mechanical clamps, it's impossible to guarantee the target object will always be in the same position. This is where positioning functionality comes in. If the positioning is inaccurate, the measuring tool's position may also be inaccurate, sometimes leading to significant deviations in the measurement results.
III. Calibration
Generally, the following calibrations are required for high-precision measurements: First, optical distortion calibration (which is usually necessary if you are not using a software lens); second, projection distortion calibration, which is the correction of image distortion caused by your installation position error; and third, object space calibration, which is to 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.
IV. The speed of the 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 speed * camera exposure time), which is not something that software can solve.
V. Measurement Accuracy of the Software
In measurement applications, the software's accuracy should be considered at 1/2 to 1/4 of a pixel, preferably 1/2, rather than 1/10 to 1/30 of a pixel as in positioning applications, because the software can extract very few feature points from images 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.
