How do you deal with highly reflective metal surfaces? It’s definitely not as simple as just dimming the lights.

The most common pitfall at metal part inspection sites is continuously adjusting the brightness without controlling the direction of reflection.

In many on-site debugging sessions, the bottleneck isn't the algorithm itself—it's the very first image. If the target doesn't appear in the image, subsequent adjustments to thresholds, filters, and models will become extremely difficult.

Case 045: Metal Part Wrinkle Detection

This case is testing...Metal Part Wrinkle DetectionA sample can be simply understood as...Metal parts, material/surface properties can be categorized asMetal/High-reflection material.

The most troublesome point on site is:Insufficient contrastThe effective solution is:Zero-degree ring illumination.

When contrast is insufficient, simply increasing brightness may not necessarily help. The key is to create a stable grayscale difference between the target and the background.

Zero-degree ring light can illuminate the target from a direction close to the lens, making it suitable for surfaces with shallow variations.

The last thing to look at isn't how beautiful the parameters are, but whether the results have become more stable—highlighting the detection features.

Such schemes also have limitations: they require real-world verification, taking into account the sample material, target size, and camera angle.

Case 054: Copper Pipe Head Scratch Detection

This case is testing...Copper Pipe Head Scratch DetectionA sample can be simply understood as...Copper pipe, material/surface properties can be categorized asMetal/high-reflection material, cylindrical or curved surface structure.

The most troublesome point on site is:Reflection interferenceThe effective solution is:High-angle brightfield illumination.

The issue of reflections cannot be solved simply by increasing exposure; the key lies in controlling the proportion of direct reflections that enter the lens.

High-angle brightfield illumination can stably illuminate the main surface, but be mindful of specular reflections from highly reflective materials.

The last thing to look at isn't how beautiful the parameters are, but whether the results have become more stable: highlight the scratches and avoid interference from surface textures.

Such schemes also have limitations: they require real-world verification, taking into account the sample material, target size, and camera angle.

Case 060: Motor Shaft Terminal Direction Detection

This case is testing...Motor Shaft Terminal Direction DetectionA sample can be simply understood as...Motor shaft, material/surface properties can be categorized asMetal/high-reflection material, cylindrical or curved surface structure.

The most troublesome point on site is:Reflection interference, insufficient lighting uniformityThe effective solution is:Backlit contour lighting.

The issue of reflections cannot be solved simply by increasing exposure; the key lies in controlling the proportion of direct reflections that enter the lens.

Backlight doesn't care about the surface color and texture; it simply turns the subject into a black-and-white silhouette.

The last thing to look at isn’t how beautiful the parameters are, but whether the results have become more stable: highlighting the contours.

Such solutions also have limitations: backlighting is more suitable for contour-based tasks but less appropriate for tasks that require observing surface textures or color differences.

How do you judge it on the spot?

If you encounter a similar issue, I don’t recommend immediately asking, “What’s the best light source to use?” A more practical way to ask would be:

1. Is the target now separated from the background?

2. After the light is changed, does it enhance only the target, or does it also enhance irrelevant textures?

3. Can this image be consistently reproduced, rather than looking good only on a specific sample?

The value of a lighting setup isn't to make the images look prettier—it's to reduce the amount of guesswork the algorithm has to do.

Summarize in one sentence.

How can we avoid glare interference on highly reflective metal surfaces? The key isn't simply to increase brightness; rather, it's about creating stable imaging differences.

The image is very bright but the detection is unstable—this kind of situation is all too common on-site. What really needs to be addressed is: Are the target features clearly highlighted, and have the interfering signals been effectively suppressed?