The plant manager called me in a panic. Their manual quality control was missing too many defects, and a major client was threatening to leave.
"Can you build something that catches everything?" he asked. "And it needs to run faster than our production line."
The Real Challenge
Here's what nobody tells you about factory computer vision: it's not about the algorithms. It's about surviving extreme heat, constant vibration, and operators who'll unplug your system to charge their phones.
The production line moved incredibly fast. Defects were tiny - some just hairline scratches. And we had to catch multiple defect types without slowing anything down.
What Actually Worked
After several failed prototypes, we landed on a simple insight: use multiple synchronized cameras instead of one expensive one. Suddenly you see everything from different angles.
The breakthrough came when Juan, one of the line operators, mentioned they could spot defects by the way light reflected off surfaces. We redesigned our entire approach around that observation - basically teaching the computer to see like Juan.
Results
The system now runs continuously, processing thousands of items with minimal downtime. The accuracy improved dramatically compared to manual inspection, and the company saw significant cost savings from reduced returns and fewer missed defects.
The only major downtime we experienced? When someone accidentally drove a forklift into our server rack.
What I Learned
The fanciest algorithms mean nothing if your system crashes in production. We spent more time on failover mechanisms than on the CV models. Every camera has a backup. Every server has a twin. Even the network switches are redundant.
The real victory wasn't the technical achievement - it was when the quality team lead told me she could finally take weekends off. That's when I knew we'd built something that mattered.