Field symptom
Black edges or obstructions appeared in some external-camera feeds.
Project 03
- Role
- Product Manager
- Focus
- AI quality analytics across a connected-camera fleet
Fleet-Wide Camera Quality Analysis with AI
Turning an invisible field problem into a measurable, fleet-wide quality signal.
AI Quality AnalyticsConnected DevicesField ReliabilityVendor Management
Fleet-wide result
6-7%
of analyzed cameras were affected
traced to specific batches and proactively replaced
Executive summary
Measure the complete population, not a convenient sample.
01
External-camera obstructions were visible in the field, but their real scale was unknown.
02
A lightweight vision-based component classified whether obstruction exceeded the acceptance criteria.
03
Fleet-wide results identified affected batches and supported proactive replacement with the vendor.
01Context
An image-quality problem was hiding in plain sight.
Some external-camera feeds across the deployed fleet showed significant black edges or physical obstructions. Individual examples were visible, but the size and distribution of the issue were not.
“A field symptom becomes a product problem when its scale, pattern and customer impact are unknown.”
02The challenge
Thousands of streams made manual review impractical.
The obvious approaches could not produce a reliable fleet-level answer. Reviewing every stream manually would consume significant time, while sampling a few hundred cameras could create a misleading picture.
Unknown scale
Thousands of deployed camera streams made the true extent difficult to see.
Manual constraint
Reviewing every stream by hand would be extremely time-consuming.
Sampling risk
A small sample could materially overstate or understate the problem.
“The product question was not whether examples existed. It was how to measure the real population accurately enough to act.”
03My role
Turn a quality concern into a decision system.
As Product Manager for the camera ecosystem, I connected the field problem, measurable quality criteria, technical analysis and vendor action.
01 / Problem framing
Reframed an anecdotal image-quality issue as a measurable fleet-wide product problem.
02 / Acceptance criteria
Defined the usable-image-area threshold and when an obstruction should be classified as unacceptable.
03 / Decision leadership
Proposed the AI-based approach and drove the analysis toward a product and vendor decision.
04Acceptance criteria
Define what usable image area means.
The analysis needed a product definition of acceptable quality. The usable frame had to be separated from black edges and obstructions, then evaluated against a consistent threshold.
Usable image area
Classification principle
Detect the usable-image boundary, measure the obstruction, and classify whether it exceeds the acceptance criteria.
05AI analysis
A lightweight component made full-population analysis possible.
The vision-based component analyzed camera streams in the field, detected the edges of the usable image and applied the same classification logic across the entire deployed population.
Camera stream
Detect usable-image edges
Measure obstruction
Apply acceptance criteria
Classify quality signal
“The value of AI was not novelty. It was making an otherwise impractical measurement repeatable at fleet scale.”
06From signal to root cause
Fleet-wide evidence created a path to action.
The analysis did more than quantify the issue. It made it possible to connect affected devices back to camera batches, identify the source and work with the vendor on targeted replacement.
Affected camera identified
Device traced
Batch correlation
Source isolated
Vendor action
01
Avoided relying on anecdotal field reports
02
Focused action on specific affected batches
03
Supported proactive replacement before widespread escalation
07Outcome & lesson
An invisible problem became a measurable quality signal.
01
Quantified the issue: roughly 6-7% of cameras were affected.
02
Traced affected devices to specific camera batches and identified the source.
03
Worked with the vendor to replace affected units proactively.
“Use AI to make the impractical measurable - not for its own sake.”