AI-Powered Industrial Quality Control

/ Manufacturing / USA / 2023-Present /

Detection Accuracy: 98-99%
Latency Reduction: 40-60%
Reduction in Manual Handling: 30%

Client: US Manufacturing Company
Industry: Manufacturing
Region: USA
Period: 2023-Present
Engagement: Dedicated Team
Team size: 60 MM

Services used

Summary

A US manufacturing company needed a vision system to automatically classify materials and detect their exact position on a moving conveyor belt - critical for triggering a PLC reject mechanism at the right moment. The system had to process two camera feeds at 14+ FPS simultaneously, exceed 95% detection accuracy, and operate 24/7 under industrial dust and vibration without human intervention.

The Challenge

Exact material position on the belt had to be detected - not just classified - to trigger PLC rejection accurately

Dual-camera setup required sustained 14+ FPS throughput per camera simultaneously

Industrial environment (dust, vibration, variable lighting) demanded model robustness around the clock

System had to integrate with the existing PLC reject mechanism without hardware replacement

The Solution

01 YOLO detection with localization

YOLO-based detection pinpoints material coordinates on the conveyor rather than only classifying what passed. Position is what the reject mechanism needs to fire at the right moment.

02 TensorRT-optimized inference

Inference optimized with TensorRT sustains 14+ FPS across two camera feeds at once, which is the throughput the dual-camera setup required from a single edge device.

03 Direct PLC integration

Detection output triggers the existing PLC reject mechanism directly, so the system slots into the line without replacing hardware that already works.

04 Hardened edge deployment

The edge GPU deployment is hardened for 24/7 operation in dust, vibration and variable lighting, which is what keeps accuracy above 95% without someone attending it.