I have been working in the image measurement industry for over twenty years, and I have encountered all kinds of workpieces as well as many unexpected “phenomena” during inspections. One common issue is that when the surface of a workpiece is relatively rough or has special coatings, the edge recognition of the image measuring instrument becomes unstable, and the measurement results show obvious deviations. This is not a problem with any specific manufacturer’s equipment but rather the result of both optical principles and material properties.

Ideally, the edges of a workpiece in an image should appear as clear grayscale transitions: from bright to dark or from dark to bright, forming a sharp gradient. With this, the edge detection algorithms in the software can capture stable signals and extract the boundary accurately. But reality is often far from ideal. For example, when aluminum parts are sandblasted, the surface is filled with tiny irregular bumps. The incident light undergoes diffuse scattering, and the grayscale distribution seen by the camera becomes chaotic. Bright and dark regions alternate, and the curve fluctuates heavily. What should have been a clean “boundary line” turns into a shaky grayscale profile.

I recall once inspecting a precision mold part whose surface had an oxide layer. The reflectivity varied drastically across regions: some areas appeared overly bright while others were very dark. As a result, the edge transition was broken up, the gradient lost stability, and the extracted boundary became blurred or even fractured. The customer was puzzled at the time, assuming it was due to insufficient instrument accuracy, but the root cause was the complex surface reflection.

Another problem brought by rough surfaces is noise. I once helped an automotive parts factory inspect sandblasted castings. When the image was magnified, it was filled with bright speckles and grainy patterns. The algorithm mistakenly treated many speckles as edge points, causing measurement deviations of tens of microns. For precision parts, such errors are absolutely unacceptable.

The situation becomes even more complicated when a metallic coating is added. Coatings tend to produce micro-reflections, creating sharp brightness differences between neighboring pixels. The image then becomes full of tiny bright and dark spots. These interference signals mix with real edges, making it difficult for the software to determine the true boundary. Often, the system either fails to detect gaps or creates “false edges.”

I also remember encountering this with smartphone frames. The anodized surface produced numerous fine reflective points in the image. The software interpreted them as part of the edge, and the calculated length was significantly larger than the actual dimension. This case made it clear to me that noise interference is far more destructive to measurement accuracy than many people realize.

After more than two decades in this field, I have realized one thing: the biggest challenge with rough surfaces and special coatings is not the quality of the equipment but the limitations of optical imaging itself. Uneven grayscale distribution makes edge signals unstable, while noise interference “confuses” the algorithm. That is the real reason why recognition rates drop. When faced with such situations, one cannot simply blame the instrument. Instead, solutions must be sought from three aspects: lighting design, material properties, and software processing. Only by understanding these principles can we truly bring out the best performance of an image measuring instrument.