Deep Learning Defect Detection System
Client Partner: AutoParts Precision · Build Timeline: 5 Months
Project Tech Stack
Allocated Engineering Pod
- ML Engineersx2
- Hardware Specialistx1
"The vision system flags micro-cracks that are completely invisible to inspectors."
Heinrich Schmidt
Factory Director
1. Context & Business Goal
The Core Challenge: Manual inspections on factory lines missed 4% of parts defects, causing parts recalls and warranty complaints.
The Agreed Objective: Build a real-time computer vision system using cameras to inspect parts on conveyer belts.
2. Technical Research & Discovery
Analyzed visual data of metal cracks, anomalies, and structural errors. Prior to writing any code, our team compiled reference data frameworks and mapped API endpoints to identify speed limits.
3. System Architecture Design
Layout Scheme: Inspection screen showing conveyer feeds, highlighting defective parts with red warnings.
Database Infrastructure Setup: Edge camera hardware running YOLOv8 models optimized via TensorRT, feeding cloud logs.
4. Production Implementation Lifecycle
Model Optimization
Compiled YOLOv8 parameters using TensorRT to run at 120fps on Edge GPUs.
Conveyer Integration
Installed optical sensors to trigger camera captures as parts passed.
5. Validated Business Outcomes
- ✓Reduced customer parts returns by 92% within 60 days.
- ✓Automated factory line scans, removing manual inspection bottlenecks.