Where Vision Fails

Pitfalls

Part of: Computer Vision Basics

Researchers once stuck a small, carefully designed sticker on a stop sign. To a human it still plainly said STOP. To a vision model, it now read "Speed Limit 45." Nothing about the sign's meaning changed, a few crafted pixels broke the model completely. Vision systems are powerful and genuinely useful, but they fail in specific, knowable ways. Knowing where they break is what separates a demo from a product you can trust. What it is A vision failure is any case where the model's output is confidently wrong because the input falls outside what it handles well. These are not random glitches; they cluster into recognizable categories. The deeper danger is that the model usually fails confidently : it returns a clean, high-confidence answer with no built-in signal that it is out of its depth. How it works, the common failure modes - Lighting and occlusion. Too dark, too bright, heavy shadow, or the object half-hidden behind something. The pixels the model relies on simply aren't there or are distorted. - Edge and out-of-distribution cases. The model only learned from its training data. A species, object, or scene it never saw, or one photographed from a weird angle, gets shoehorned int

Challenge: The Vision Trust Gate