In vision measuring, the first step is locating the edge. If the edge detection is off, every measurement after that will be inaccurate. Many only rely on running a Canny or Sobel algorithm, but in practice, it’s not that simple. Here are some methods we often use that are proven stable in the field.

Get the Lighting Right First
No matter how good the algorithm is, bad lighting produces noisy edge signals. Backlight, coaxial, or ring lights must be chosen correctly—this step is often overlooked.

Use Projection to Pinpoint Rough Positions
Projecting along the X or Y axis compresses pixel rows into waveforms, where peaks and valleys reveal edge positions. This method averages out small noise, making it more stable than filtering raw images directly.

Differentiation: Old but Reliable
Differentiating the projection curve highlights points where changes are largest—the edges. This old-school method is more resistant to shop-floor noise than some high-end algorithms.

Subpixel Accuracy for True Precision
Once the edge is found, accuracy matters. Taking three to five points near the peak and fitting them with a quadratic curve yields coordinates at 1/100 pixel precision, essential for micron-level measurements.

Light Reflection: Edge Breaks
On aluminum or stainless steel, reflections can cause edge breaks. Use a polarizing filter or adjust lighting angles to avoid glare.

Busy Backgrounds: False Edges Everywhere
If the background color is close to the workpiece, false edges appear. Change the background color or use morphological processing to remove it first.

High Noise: Jagged Edges
When grayscale fluctuates heavily, edges become jagged. Apply Gaussian or median filtering before differentiation to stabilize the signal.

The “Bright Spot” Battle with an Aluminum Case
We once measured an aluminum housing where lighting caused bright spots everywhere, and measurements drifted by tens of microns. The fix:

Switched to coaxial lighting with a polarizing filter

Used projection + differentiation for edge detection

Applied subpixel fitting for precise localization

After adjustments, repeated measurements showed variations under ±1 µm.

Lighting, Algorithms, Positioning—All Matter
Edge detection in vision measuring isn’t mystical, but details matter. Lighting, algorithms, and positioning must work together. Don’t just tweak software parameters—test multiple setups on-site to find the most stable one, so your measurements stay trustworthy.