ClientAI & Machine Learning•2024
Computer Vision Detection System
Custom YOLO-based detection system for quality inspection, safety monitoring, and automated counting in manufacturing environments. Deployed on edge devices for real-time inference.
Project Overview
Custom YOLO-based detection system for quality inspection, safety monitoring, and automated counting in manufacturing environments. Deployed on edge devices for real-time inference.
Problem Statement
Manufacturing facilities need automated visual inspection for quality control and safety compliance, but off-the-shelf solutions are expensive and lack customization for specific use cases.
Proposed Solution
Custom-trained YOLOv8 models optimized for edge deployment, with a management dashboard for model monitoring, retraining triggers, and inference logging.
Key Features
Custom object detection for specific defect types
Real-time inference at 30+ FPS on edge devices (Jetson Orin)
Multi-camera support with synchronized inference
Automated alerting for safety violations
Production counting and throughput tracking
Model performance monitoring and drift detection
Active learning pipeline for continuous improvement
Integration with PLC/SCADA systems
Web-based management dashboard
On-premise deployment with air-gapped support
Technology Stack
PythonPyTorchYOLOv8OpenCVONNX RuntimeTensorRTFastAPIReactTypeScriptPostgreSQLTimescaleDBDockerNVIDIA Jetson
Project Screenshots
Development Process
01
Data Collection
3 weeks- Camera setup and calibration
- Dataset collection (50K+ images)
- Annotation with domain experts
02
Model Training
4 weeks- YOLOv8 training and hyperparameter tuning
- TensorRT optimization for edge
- Accuracy vs latency tradeoff analysis
03
System Integration
5 weeks- Edge deployment pipeline
- Dashboard development
- PLC integration
- Alerting system
04
Deployment
2 weeks- On-site installation
- Operator training
- Monitoring setup
- Documentation
Challenges
- Achieving real-time performance on edge hardware
- Handling variable lighting conditions in factory environments
- Managing model drift with limited labeled data
- Integrating with legacy industrial systems
Results & Outcomes
- 99.2% defect detection accuracy
- 60% reduction in manual inspection labor
- Sub-50ms inference latency on Jetson Orin
- Zero safety incidents since deployment