Machine Vision Measurement Accuracy

Introduction

Machine vision, being a rapidly evolving field at the junction of computer science and optics, has transformed industries stretching from industrial to healthcare. One of the key aspects of machine vision systems is their capability to accurately measure objects and their attributes. This precision is crucial for quality control, programmed assembly, and various other applications. However, attaining high measurement accuracy in machine vision systems is a difficult bid that involves considerations of hardware, software, calibration, and environmental factors.

The Significance of Measurement Accuracy

Accurate measurements are the basis of many manufacturing processes. In industry, for instance, even the slightest variation from a specified factor can lead to product defects, wasted materials, and costly rework. Moreover, precise measurements are crucial for ensuring compliance with regulatory standards and maintaining product quality. In fields like medical imagery and robotics, measurement accuracy is the basis for safe and effective operation.

Accuracy, repeatability, and resolution are three main metrics by which any measurement tool is rated, including most machine vision systems. Unfortunately, there’s a great deal of confusion around these metrics, particularly regarding how they are interrelated and work together. 

To begin a discussion about these terms, it's first necessary to define each of them:

Accuracy is the degree of conformance between a measurement of an observable quantity and a recognized standard or specification that indicates the true value of the quantity. Accuracy is a qualitative concept (i.e., one in which a process is based on physical properties) and should not be confused with precision, which is the closeness of agreement between independent test results obtained by applying a measurement procedure under stipulated conditions.

Repeatability of a measurement tool is the amount by which repeated measurements of the same quantity and same operating conditions vary from their mean. In some situations, repeatability can be defined as the value below which the absolute difference between two single test results obtained under identical measurement conditions may be expected to lie within a specified probability.

Resolution refers to the smallest optical feature, such as a line that can be distinguished from another line in an optical system. Resolution depends on several factors:

· The number of pixels in an image sensor.

· The magnification of how those pixels are projected onto an object.

· The quality of the optics that map the image to the image sensor.

How Accuracy and Repeatability Are Interrelated

The accuracy of a process cannot be correctly calculated without first determining the repeatability of the measurement process. Repeatability is a prerequisite for accuracy. If the measurement system cannot produce repeatable measurements, the measurement results can’t be verified as accurate.

Many machine vision systems will be able to function with a measurement process that is repeatable without being accurate (e.g., changes in the measured diameter don’t directly correspond with the changes in the object’s diameter). Even if a repeatable measurement system doesn’t yield accurate measurements, it will perform in the same manner under the same conditions, allowing relative measurements to be made.

But often overlooked is the opportunity—and sometime necessity—of performing object measurements to ensure proper quality control or process improvements. To get these object measurements—as well as to obtain valid operating data to form the basis for improvements—repeatable measurements must be calibrated to become accurate, yielding measurements that closely reflect the true value being measured.  

The process of elevating a repeatable measurement system to one that is repeatable and accurate depends on the application and budget.

How Accuracy and Resolution Are Interrelated

Once a video signal is digitized, the resulting digital data is stored in memory and can be accessed by an image processor. The number of pixels in an image defines the resolution of an image, and the number of bits used to store the brightness information defines the depth of the image.

Each pixel has a numerical value that represents the average brightness of the image over the area that the pixel occupies in the original analog image, where 0 represents black, 255 represents white, and the values between represent all the shades of gray (for an 8-bit memory buffer). Although the camera has discrete physical pixels, the exact timing between pixels is lost due to the time delay between transmitting that pixel and digitizing its value, causing a temporal error.

By increasing the resolution of the image by using a larger frame buffer, a measurement can be made more accurate up to the point of the size of the camera sensor. Based on Nyquist's sampling theory, we can say that a 768 X 576 pixel camera can display up to 384 X 288 distinct lines, or objects in the image, under ideal conditions. (A typical image has much less information content than this number.)

Because physical pixels are not the same sizes horizontally and vertically, and the digitization process does not occur on pixel boundaries, any pixel-based measurements do not give the same calibration value horizontally and vertically. Therefore, the correlation between the photosites (camera pixels) and the digitally stored pixels can only be approximated by calibrating the pixel size in both the horizontal and vertical directions.

Elements of Measurement Accuracy

1. Hardware Components

The hardware sections of a machine vision system play a primary role in defining measurement precision. High-quality cameras with right resolution and vibrant range are essential for capturing clear and detailed images. Furthermore, precision optics and lighting systems are critical for lowering distortion, glare, and other artifacts that can affect measurements.

2. Software Processes

The algorithms active in machine vision systems are needed for achieving accurate measurements. Image processing methods such as edge detection, pattern identification, and feature mining are employed to obtain relevant information from the captured images. Moreover, advanced algorithms like machine learning models can improve measurement accuracy by education patterns and making intelligent choices based on training data.

3. Standardization and Correction

Standardization is a fundamental step in ensuring measurement accuracy. It comprises locating the machine vision system with a known reference standard. This process corrects any inherent biases, lens alterations, or pixel abnormalities in the imaging system. Regular standardization is vital to maintain accuracy over time, as environmental conditions and wear on equipment can cause implication.

4. Environmental Factors

Environmental factors, for example humidity, temperature, humidity can substantially impact measurement accuracy. Changes in ambient conditions can cause materials to expand or contract, affecting their dimensions. Sufficient lighting, uniform across the field of view, is vital to avoid shadows and reflections that can alter measurements.

Challenges in Getting High Measurement Precision

1. Resolution vs. Speed Trade-off

A general question in machine vision systems is reaching the right balance between resolution and managing speed. Greater resolution permits for smaller details to be taken, enabling more accurate measurements. However, this can come at the cost of slower processing times, which may not be viable in applications where real-time measurements are vital.

2. Managing Variability and Noise

Real-world objects often exhibit changeability in shape, color, and texture. Machine vision systems must be strong enough to handle these differences while still providing precise measurements. Furthermore, noise in the imaging process, whether from sensor restrictions or environmental reasons, can introduce errors in measurements.

3. Vivid Surroundings

In industries like manufacturing, where objects are in continuous motion, capturing accurate measurements can be particularly challenging. Pursuing and compensating for movement requires sophisticated processes and focused hardware.

Future Developments and Technologies

The search for higher measurement precision in machine vision continues to drive innovation. Rising technologies such as 3D imaging, multispectral imaging, and time-of-flight sensing hold pledge for overcoming current constraints. In addition, the integration of artificial intelligence and machine wisdom is presumed to lead beyond adaptive and intelligent measurement systems.

Conclusion

Achieving high measurement precision in machine vision systems is a versatile bid that requires a combination of advanced hardware, mature software algorithms, accurate calibration, and consideration of environmental circumstances. Striking a balance between precision and realism is essential, as real-world applications often demand a trade-off between resolution and speed. As technology stays advanced, the future of machine vision measurement precision looks promising, with the potential to reform industries and applications across the board.