You will get a custom computer vision solution using YOLO and OpenCV
Project details
You will get a custom computer vision solution using YOLO, OpenCV, and Python.
I can help you build an object detection prototype, real-time video processing pipeline, RTSP/CCTV analytics system, or custom computer vision integration depending on the selected package.
This project can be used for object detection, safety monitoring, video analytics, image processing, event detection, tracking, alerts, and proof-of-concept AI vision systems.
My experience includes real-world computer vision systems such as CCTV analytics, fire/smoke detection, intrusion and loitering detection, license plate recognition, autonomous driving perception, and edge AI deployment. I focus on building practical vision systems that are clear, maintainable, and ready to test with real data.
I can help you build an object detection prototype, real-time video processing pipeline, RTSP/CCTV analytics system, or custom computer vision integration depending on the selected package.
This project can be used for object detection, safety monitoring, video analytics, image processing, event detection, tracking, alerts, and proof-of-concept AI vision systems.
My experience includes real-world computer vision systems such as CCTV analytics, fire/smoke detection, intrusion and loitering detection, license plate recognition, autonomous driving perception, and edge AI deployment. I focus on building practical vision systems that are clear, maintainable, and ready to test with real data.
AI Development Type
Deep Learning, Model Tuning, Software MaintenanceAI Tools
NVIDIA AI Platform, Open Neural Network Exchange, OpenCV, PyTorchAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$200
|
Standard
$600
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 25 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | |
Model Documentation | - | - | |
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$50Frequently asked questions
About Shokhrukh
AI Automation & Computer Vision Engineer | RAG, YOLO, Edge AI
Incheon, South Korea - 12:34 am local time
I have production-level experience in computer vision, deep learning, embedded/edge AI, and multimodal AI systems. My work includes CCTV analytics, object detection, fire/smoke detection, intrusion detection, autonomous driving perception, smart city analytics, and real-time edge deployment.
𝗪𝗵𝗮𝘁 𝗜 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝘄𝗶𝘁𝗵:
▪ 𝗥𝗔𝗚 𝗰𝗵𝗮𝘁𝗯𝗼𝘁𝘀 for documents, PDFs, websites, and internal knowledge bases
▪ 𝗔𝗜 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 using Python, APIs, n8n/Make, OpenAI/LLM tools, and workflow systems
▪ 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝘃𝗶𝘀𝗶𝗼𝗻 systems using YOLO, OpenCV, RTSP streams, and CCTV cameras
▪ 𝗢𝗯𝗷𝗲𝗰𝘁 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻, segmentation, tracking, and video analytics
▪ 𝗘𝗱𝗴𝗲 𝗔𝗜 deployment and optimization for real-time inference
𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘀𝘁𝗮𝗰𝗸:
Python, C++, FastAPI, OpenCV, YOLO, PyTorch, TensorRT, ONNX, Docker, Qdrant, vector databases, REST APIs, LLMs, RAG, RTSP, CCTV, Jetson/Edge AI.
I have worked on real-world AI systems, including autonomous driving perception, safety monitoring, smart city analytics, and embedded AI deployment. I focus on building systems that are practical, maintainable, and ready to use.
If you need an AI prototype, MVP, RAG chatbot, computer vision pipeline, or production-ready AI system, I can help you design, build, and deploy it.
Steps for completing your project
After purchasing the project, send requirements so Shokhrukh can start the project.
Delivery time starts when Shokhrukh receives requirements from you.
Shokhrukh works on your project following the steps below.
Revisions may occur after the delivery date.
Review visual data and goals
I review your sample images/videos, target objects, expected output, and deployment environment.
Design the vision pipeline
I define the detection workflow, model approach, preprocessing, tracking, and output format.