You will get a real-time computer vision detection system with YOLO
Project details
Most computer vision projects fail not because of the model — but because nobody tested it against real-world garbage: bad lighting, motion blur, partial occlusions, edge cases that only show up in production.
I build systems that actually work outside the notebook. At Kohinoor Textile Mills, I deployed a 47-camera real-time visual monitoring platform using NVIDIA DeepStream, TensorRT, and YOLOv8 — tracking 17,000+ people and 2,000+ vehicles daily, integrated directly with Oracle ERP dashboards. That system runs 24/7 in an industrial environment where failure is not an option.
What you get from me is not a demo. It is a tested, documented, production-ready detection pipeline built around your specific failure modes — with source code you can actually maintain.
I work on object detection, people and vehicle tracking, OCR, ANPR, and custom classification problems. If your data is messy, your environment is tough, or your previous solution kept breaking — that is exactly the kind of problem I am built for.
I build systems that actually work outside the notebook. At Kohinoor Textile Mills, I deployed a 47-camera real-time visual monitoring platform using NVIDIA DeepStream, TensorRT, and YOLOv8 — tracking 17,000+ people and 2,000+ vehicles daily, integrated directly with Oracle ERP dashboards. That system runs 24/7 in an industrial environment where failure is not an option.
What you get from me is not a demo. It is a tested, documented, production-ready detection pipeline built around your specific failure modes — with source code you can actually maintain.
I work on object detection, people and vehicle tracking, OCR, ANPR, and custom classification problems. If your data is messy, your environment is tough, or your previous solution kept breaking — that is exactly the kind of problem I am built for.
Machine Learning Tools
NVIDIA AI Platform, OpenCV, Python, PyTorch, Tesseract OCRWhat's included
| Service Tiers |
Starter
$50
|
Standard
$100
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 7 days |
Number of Revisions | Unlimited | Unlimited | 5 |
Number of Model Variations | 2 | ||
Model Validation/Testing | - | ||
Model Documentation | - | - | |
Data Source Connectivity | - | - | - |
Source Code | - |
About Muavia
AI/ML Engineer | Industrial Computer Vision & Oracle ERP Integration
Rawalpindi, Pakistan - 5:09 pm local time
I have more than three years of experience in computer vision, OCR, and predictive modeling during which I have designed and implemented AI pipelines for industrial surveillance, automation, and smart data analytics. The project I am currently working on at Kohinoor Textile Mills is the construction of an in-house Visual AI Platform that connects with 49+ live cameras, utilizing NVIDIA DeepStream, TensorRT, and PyTorch, and directly integrated with Oracle APEX for real-time reporting dashboards.
What I Deliver:
1. Complete ML pipelines — data prep → model training → deployment
2. Computer vision / OCR for text & object detection (YOLOv8, EasyOCR, Tesseract)
3. Real-time video analytics & DeepStream optimization on GPU
4. Model optimization for speed, accuracy, and cost efficiency
5. Seamless integration with databases, dashboards, and edge devices
Highlights
1. More than 20 ML models deployed that result in improved accuracy and reduced inference time
2. ANPR/OCR systems optimized for complex industrial lighting conditions and angles
3. Real-time results (<200 ms latency) achieved on GPU-accelerated pipelines
4. A significant record of in-house cost savings due to automation and AI application
If you require a specialist who can convert your chaotic data or camera feeds into a functional AI system that produces quantifiable results — let's create it together.
Steps for completing your project
After purchasing the project, send requirements so Muavia can start the project.
Delivery time starts when Muavia receives requirements from you.
Muavia works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Requirements
Understand your detection goals, review your data, and define the pipeline scope.
Model Training & Testing
Train or fine-tune YOLO model on your data, validate accuracy, fix edge cases.