Computer vision systems often look impressive in controlled demonstrations. Clear imagery, stable lighting, reliable connectivity, and carefully curated datasets produce results that appear close to solved.
Deployment tends to tell a different story.
In operational environments, cameras move, lighting changes unexpectedly, compression alters detail, and bandwidth limits force difficult trade-offs. Models that perform well in testing begin to lose context, producing confident answers at precisely the moments uncertainty is highest.
We repeatedly encountered this while working with autonomous platforms and distributed sensing systems. The problem was rarely model accuracy in isolation. It was the gap between perception and environment. A vision model does not see the world. It sees assumptions embedded in data. When those assumptions change, performance can degrade quickly and often silently. Rain alters contrast, vibration shifts perspective, thermal drift affects sensors, and network delays break temporal continuity. Individually these effects are small. Together they change how a system interprets reality. This is why many real deployments struggle. Engineering effort is often focused on improving detection accuracy, while the harder problems sit elsewhere: synchronising imagery with telemetry, deciding what must be processed locally versus remotely, and maintaining reliable behaviour when inputs become incomplete.
Our approach gradually shifted as a result. Instead of treating computer vision as a standalone AI component, we began integrating perception directly into system context. Vision outputs are combined with state estimation, timing information, and operator intent so uncertainty can be managed rather than ignored.
In practice this means accepting that perception is probabilistic. Systems must degrade gracefully, signal uncertainty clearly, and remain useful even when confidence drops. Edge deployment becomes as important as model selection, and integration often matters more than raw inference speed.
We are still refining these techniques, but one conclusion has become clear. Computer vision succeeds in the field not when models become perfect, but when systems are designed to cope with imperfection. As sensing becomes more widespread across infrastructure, autonomy, and distributed operations, perception will increasingly depend on how well visual understanding is connected to reasoning and coordination systems around it.
We continue to explore these challenges through ongoing work in autonomous systems, infrastructure sensing, and applied AI platforms. If you are dealing with perception problems that behave very differently outside the lab, we are always interested in comparing notes and exploring practical approaches together.
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