AI Defect Detection: What Data You Need and What Accuracy Hides
How much data depends on how rare the defect is. A worked example of why a 98.8 percent accurate model can still miss one defect in five.
Co-Founder of Dev Station Technology
linkedin.com/in/leenguyen-devstation
Lee Nguyen co-founded Dev Station Technology in July 2023. He leads solution design and delivery for BLE-connected test equipment, which means torque gauges, pull testers, load cells and digital gauges feeding straight into offline-first mobile apps. His clients are inspection, lifting, testing and field service companies in the UK and the US.
The direction came from one job on site. A UK engineer finished an anchor pull test in four minutes, then spent three hours retyping readings, matching photos to anchors from memory and formatting a PDF. In his words, the test was never the bottleneck.
On the platforms he has delivered since, report turnaround has gone from around three hours to about twenty minutes. He also runs dedicated offshore development centres for clients who need engineers embedded long term, from team ramp-up through delivery governance.
Stack: .NET MAUI, Xamarin, BLE and GATT, Azure, REST APIs, plus embedded and firmware integration.
Hydrajaws Limited makes portable pull testers in the UK and wanted an app that could read its own instruments. Verify Auto covers automotive safety compliance testing. The self-hosted IoT platform was built on an open source base so the client owns every device, dashboard and data record.
Closest service pages to his work: digital quality inspection, gauge and instrument capture, and dedicated offshore development centres.
If your team still moves test data by hand, or your gauge vendor stops at showing a number on screen, send him the workflow. Write to lee@dev-station.tech or talk to Dev Station.
How much data depends on how rare the defect is. A worked example of why a 98.8 percent accurate model can still miss one defect in five.
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