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AI Case Study · Computer Vision & Machine Learning
AI Defect Detection Hits 96% Accuracy in Auto Production
A Tier-1 automotive paint shop needed to catch sub-millimetre surface defects without adding conveyor stops, extra inspectors or cloud latency.
Computer VisionVisual InspectionEdge AIDeep LearningPLC Integration
96%Detection accuracy
-42%False positives
180 msEdge inference
€2.4k/dayRecovered uptime value
Project Snapshot
Edge computer vision for automotive surface inspection
The system had to find tiny paint defects at production speed while integrating directly with existing factory controls.
IndustryAutomotive manufacturing
Production Volume1,200 bodies/day
Training Dataset200,000 images
Delivery Window8 weeks
Edge RuntimeNVIDIA Jetson AGX
Primary Outcome96% detection accuracy
Plant Context
Inspection had become the bottleneck on a 1,200-body daily line
The plant ran three high-speed paint lines with 480 staff and supplied Class-A finishes to global OEMs. Manual inspectors were expected to catch defects down to 0.1 mm while maintaining takt time.
Production Volume
1,200 painted car bodies per day across two 10-hour shifts.
Quality Standard
Class-A surface finish with a 0.1 mm tolerance.
Operational Constraint
Inspection accuracy had to improve without slowing a 5.4 m/min conveyor.
Baseline
Manual inspection was producing both misses and unnecessary stops
81%Defect-detection accuracy
5.2%False-positive rate
19 min/dayUnplanned downtime
38 kWh/shiftRe-spray energy
AmberOEM audit rating
“We kept stopping the line for ghosts and still shipped micro scratches the customer could spot.”
Success Criteria
The model had to improve quality without slowing takt time
Success was measured against factory-level thresholds before the system was moved from shadow mode into production.
≥ 95%True-positive capture
≤ 3%False-positive rate
≤ 200 msEdge inference
10% fewerkWh per body target
Solution Architecture
Vision inference moved to the control cabinet
Two line-scan cameras fed a ResNet-50 model running on an NVIDIA Jetson AGX module. Decisions were returned directly to the Siemens S7 PLC over Profinet, with heatmaps shown on the HMI for operator verification.
01 · CaptureTwin 12 MP line-scan cameras inspect each body panel.
02 · PreprocessFrames are normalized for paint color, lighting and line conditions.
03 · InferResNet-50 scores surface defects on Jetson AGX in about 180 ms.
04 · ControlProfinet sends a binary decision to the Siemens S7 PLC.
05 · VerifyOperators see defect heatmaps on the HMI before action.
Implementation
200,000 labelled images, active learning and a weekly retraining loop
Dataset
200,000 high-resolution images covered 12 defect categories and difficult paint/lighting conditions.
Model Engineering
ResNet-50 was fine-tuned with focal loss and evaluated on held-out production imagery.
Operations
15 technicians were trained to calibrate cameras and run weekly retraining as new conditions appeared.
Services & Capabilities
Capabilities used in the engagement
The solution combined computer vision, machine learning, edge deployment and factory-system integration as one production workflow.
Results
Quality improved while line interruptions fell
81% → 96%Detection accuracy
5.2% → 3.0%False positives
19 → 11 minDowntime per shift
149 daysPayback period
-18%Paint-booth energy use
-12 t CO₂/yrEmissions avoided
Technology
Production stack
12 MP Line-Scan CamerasNVIDIA Jetson AGX XavierPyTorch 2.1TensorRT 8.6DockerUbuntu CoreSiemens S7 PLCProfinetOPC UA
Production Controls
Designed for factory operations
- Automatic switch to manual inspection if the inference path exceeds the defined failover threshold.
- Images remain on-premises with encrypted storage and controlled access.
- Weekly offline retraining handles lighting drift, new paint colors and changing defect patterns.
- Published case study reports the plant passed its OEM audit with zero surface-quality issues after launch.
Computer Vision & Machine Learning
Build visual inspection around your real line conditions
Design the camera setup, training data, model, edge runtime and control-system integration as one production system.
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