What problems is coaxial light best suited for? Don't treat it as a universal light source.

Coaxial illumination is very common in the field, but it’s not a panacea. Used correctly, it can effectively suppress the background; used incorrectly, however, it can end up suppressing the target along with the background.

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.

This article won’t delve into complicated formulas; instead, let’s approach it the way we’d look at it on an actual construction site: Where exactly was the original image problematic, and how does the situation stabilize after the light change? Also, what are the limitations of this approach?

Case 021: PCB Copper Exposure Detection

This case is testing...PCB Copper Exposure DetectionA sample can be simply understood as...PCB board, material/surface properties can be categorized asMetal/high-reflection materials, electronic components.

The most troublesome point on site is:Insufficient contrastThe effective solution is:Coaxial Brightfield 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.

Coaxial brightfield is suitable for handling planar reflections and background textures, as it makes the primary reflection direction more controllable.

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

Such schemes also have limitations: Coaxial illumination works effectively for mirror-like and flat samples, but may produce uneven illumination on strongly curved or highly irregular surfaces.

Case 042: Wire Bonding Inspection

This case is testing...Welding Wire InspectionA sample can be simply understood as...LED lamp holder, material/surface properties can be categorized asElectronic components.

The most troublesome point on site is:Target feature is unstable.The effective solution is:Coaxial Brightfield Illumination.

The target is sometimes obvious, sometimes not; usually, this indicates that the lighting hasn't yet captured the stable features.

Coaxial brightfield is suitable for handling planar reflections and background textures, as it makes the primary reflection direction more controllable.

The last thing to look at isn't how beautiful the parameters are, but whether the results have become stable: Only after the target features stabilize can the backend algorithms have a reusable basis for making judgments.

Such schemes also have limitations: coaxial illumination works effectively for mirror-like and flat samples, but may produce uneven illumination on strongly curved surfaces or samples with significant height variations.

Case 043: Fingerprint Scanner Adhesive Overflow Detection

This case is testing...Fingerprint Sensor Adhesive Overflow DetectionA sample can be simply understood as...Fingerprint sensor, material/surface properties can be categorized asGeneral industrial sample.

The most troublesome point on site is:Target feature is unstable.The effective solution is:Coaxial Brightfield Illumination.

The target is sometimes obvious, sometimes not; usually, this indicates that the lighting hasn't yet captured the stable features.

Coaxial brightfield is suitable for handling planar reflections and background textures, as it makes the primary reflection direction more controllable.

The last thing to look at isn't how beautiful the parameters are, but whether the results have become more stable: After the glue area and the background form a distinct contrast, does the judgment become more reliable?

Such schemes also have limitations: Coaxial illumination works effectively for mirror-like and flat samples, but may produce uneven illumination on strongly curved surfaces or samples with significant height variations.

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.

What problems is coaxial illumination best suited to solve? The key isn't simply increasing brightness—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?