The benefits of strip-side lighting are straightforward: it is particularly sensitive to height differences, edges, and directional textures.

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, filtering, 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 lighting is changed? Also, what are the limitations of this approach?

WIFI Shell Character Detection

This case is testing...WIFI Shell Character DetectionA sample can be simply understood as...WIFI casing, material/surface properties can be categorized asPlastic/composite materials, cylindrical or curved structures.

The most troublesome point on site is:Insufficient lighting uniformityThe effective solution is:Strip-side lighting.

Uneven brightness can cause the same target to appear different depending on its location, and the detection threshold is prone to drift.

Strip-side lighting is highly sensitive to directionality and is ideal for highlighting differences in elevation, edges, and slender structures.

The last thing to look at isn't how beautiful the parameters are, but whether the results have become more stable: After separating the characters from the background, the recognition process becomes much more stable.

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

IC Chip Pin Flatness Inspection

This case is testing...IC Chip Pin Flatness InspectionA sample can be simply understood as...IC chip, material/surface properties can be categorized asElectronic components.

The most troublesome point on site is:Target feature is unstable.The effective solution is:Strip-side lighting.

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

Strip-side lighting is highly sensitive to directionality and is ideal for highlighting differences in elevation, edges, and slender structures.

The last thing to look at isn’t how beautiful the parameters are, but whether the results have become more stable: high precision in stitch flatness.

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

LCD character detection

This case is testing...LCD character detectionA sample can be simply understood as...Calculator LCD screen, material/surface properties can be categorized asGlass/Transparent Materials.

The most troublesome point on site is:Reflection interference, insufficient contrastThe effective solution is:Strip-side 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.

Strip-side lighting is highly sensitive to directionality and is ideal for highlighting differences in elevation, edges, and slender structures.

The last thing to look at isn't how beautiful the parameters are, but whether the results have become more stable: After separating the characters from the background, the recognition process becomes much more stable.

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

Network port pin spacing detection

This case is testing...Network port pin spacing detectionA sample can be simply understood as...Network port, material/surface properties can be categorized asGeneral industrial sample.

The most troublesome point on site is:Background interferenceThe effective solution is:Strip-side lighting.

The biggest problem with images of this type isn't darkness—it's the excessive amount of irrelevant background. First, suppress the background; it’s more reliable than trying to enhance it later through algorithmic processing.

Strip-side lighting is highly sensitive to directionality and is ideal for highlighting differences in elevation, edges, and slender structures.

The last thing to look at isn't how beautiful the parameters are, but whether the results have become more stable: Only after the edges have become cleaner can measurements and positioning establish a stable foundation.

Such schemes also have limitations: they require field verification through actual shooting, taking into account the sample material, target size, and camera angle.

How does strip-side lighting highlight differences in height and directional characteristics? The key isn't simply to increase brightness; rather, it's about creating stable imaging contrasts.