Machine Vision Lighting
Blog post from Roboflow
Industrial machine-vision accuracy can decline substantially when uncontrolled ambient light from windows, fixtures, nearby equipment, or moving personnel alters brightness, shadows, reflections, color, and exposure in captured images. Common issues include glare that hides defects on reflective surfaces, sunlight that varies by time of day, and gradual hardware degradation such as dimming LEDs, dusty lenses, or shifted camera mounts. Recommended remedies include repositioning or diffusing lights, using polarizers, filters, covers, fixed mounts, and reference targets to stabilize and monitor image quality, while collecting additional training data for unavoidable environmental variation. The article advises testing whether a human can see a defect under worst-case lighting before deciding between hardware improvements and model training, since training cannot recover visual detail absent from the image. It also recommends investigating production images, capture conditions, and physical installation before retraining a model, with Roboflow AI1, Workflows, and Vision Events presented as tools for edge inference, focus monitoring, and production-image review.
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