You will get Real-time computer vision optimized for NVIDIA Jetson
Project details
I will optimize and deploy your computer vision pipeline on NVIDIA Jetson for reliable real-time processing.
The project can include ONNX export, TensorRT acceleration, camera or RTSP input, object detection, tracking, DeepStream/GStreamer integration, benchmarking, and deployment documentation.
With more than 15 years of software-engineering experience, I focus on delivering maintainable applications rather than isolated demonstration notebooks. Your solution will be tailored to the exact Jetson model, JetPack version, camera source, input resolution, performance goals, and integration environment.
Depending on your package, you will receive tested source code, optimized model files, benchmark results, setup instructions, and deployment documentation.
The project can include ONNX export, TensorRT acceleration, camera or RTSP input, object detection, tracking, DeepStream/GStreamer integration, benchmarking, and deployment documentation.
With more than 15 years of software-engineering experience, I focus on delivering maintainable applications rather than isolated demonstration notebooks. Your solution will be tailored to the exact Jetson model, JetPack version, camera source, input resolution, performance goals, and integration environment.
Depending on your package, you will receive tested source code, optimized model files, benchmark results, setup instructions, and deployment documentation.
AI Development Type
Deep Learning, Model TuningAI Tools
MLflow, Open Neural Network Exchange, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$200
|
Standard
$550
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 14 days |
Number of Revisions | 1 | 1 | 1 |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
About Ayman
Computer Vision Engineer | YOLO26, LiDAR, 3D Vision & Edge Deployment
Alex, Egypt - 10:43 am local time
Computer Vision Capabilities:
• Object detection with YOLO26, YOLO11, YOLOE and RT-DETR
• Semantic, instance and panoptic segmentation
• SAM 3 promptable image/video segmentation and tracking
• Open-vocabulary detection and segmentation
• Multi-object tracking, re-identification and video analytics
• Pose estimation, keypoint detection and activity analysis
• Oriented object detection for aerial and industrial imagery
• OCR, document vision and structured visual extraction
• Defect detection and automated visual inspection
• Face detection, recognition, verification and liveness detection
• Image enhancement, restoration and super-resolution
• Optical flow, motion estimation and scene-flow analysis
• Monocular/stereo depth estimation and depth completion
• Camera calibration, feature matching and visual localization
• 3D reconstruction, photogrammetry and multi-view geometry
LiDAR and 3D Perception:
• Point-cloud processing, filtering, registration and segmentation
• LiDAR-based 3D object detection and tracking
• PointPillars, CenterPoint, PointNet++, SECOND and PV-RCNN
• Bird’s-eye-view perception and occupancy mapping
• Camera–LiDAR–radar calibration and sensor fusion
• BEVFusion and multimodal 3D perception
• Visual SLAM, LiDAR SLAM and visual-inertial odometry
• NeRF and 3D Gaussian Splatting
• KITTI, nuScenes, Waymo and custom 3D datasets
Technologies and Deployment:
• Python, C#, C++, OpenCV, PyTorch and Ultralytics
• Detectron2, MMDetection and MMDetection3D
• NVIDIA Jetson, DeepStream, Isaac ROS and ROS 2
• ONNX, TensorRT, CUDA, OpenVINO, CoreML and TFLite
• Real-time processing with FFmpeg and GStreamer
• Cloud deployment, APIs and enterprise application integration
• SQL Server, database design and data management
• Desktop integration using C# and WPF
My extensive software-engineering background enables me to move beyond experimental prototypes and deliver maintainable applications, APIs, data pipelines, user interfaces, and deployment-ready vision systems.
I am currently pursuing an MSc in Computer Science, strengthening my academic and practical expertise in advanced computing and scalable system development.
Certifications:
• MCTS — Microsoft Certified Technology Specialist
• MCPD — Microsoft Certified Professional Developer
Steps for completing your project
After purchasing the project, send requirements so Ayman can start the project.
Delivery time starts when Ayman receives requirements from you.
Ayman works on your project following the steps below.
Revisions may occur after the delivery date.
Optimize, integrate, test, and deliver the Jetson vision pipeline
I review your hardware and requirements, convert and optimize the model, build the real-time pipeline, benchmark it on the target Jetson environment, and deliver the agreed code, model files, results, setup instructions, and documentation.