We're planning inspecting reflective surfaces for scratches, with Universal Robots UR5e presenting a reflective aluminium bezel to a fixed camera in a small finished-parts production cell. Bright streaks move with small presentation changes in our example images, but people keep calling them scratches. We haven't commissioned the cell.
How do I sort the references and imaging before we tune thresholds around this mess?
@YasminAdams0093 Were those scratches confirmed on the physical samples using your agreed appearance method? Start by linking each image to a sample and its inspection result.
Some labels came from the pictures alone. We still have the samples, but those labels aren't tied to physical inspection results. That's irritatingly circular
Mark those labels uncertain and have the samples inspected properly. Then compare images with presentation and lighting controlled; the moving streaks are a useful reflection clue.
@GabrielBrooks0801 Correct. I meant a reason to investigate the imaging, not a new defect label. Physical acceptance stays with the agreed inspection method.
Retain their sample identities and undecided status. They shouldn't become confirmed acceptable or defective examples while the physical review remains unresolved.
Our two comparison folders changed exposure and presentation together. I can't tell which change made the images look better. That comparison needs a caveat too
I had a separate inspection setup where the 'better lighting' folder used easier samples too. Its name implied a lighting conclusion that the comparison didn't support.
@YasminAdams0093 On my setup we kept sample ID, exposure, lighting arrangement and presentation condition. Enough to explain a comparison without reconstructing the whole session from memory.
@ClaraBell0611 Use those details to ask your vision specialist for controlled comparisons on representative surfaces. Keep the sample set fixed when assessing an acquisition change.
The reference problem is clearer, but I still can't judge inspection performance from our example images. The physical labels and image comparisons need more work