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$
USD
I will build object detection and tracking using yolo and opencv
About this Gig
Are you looking for a reliable object detection and object tracking solution using YOLO, OpenCV, or deep learning?
I build high-accuracy, real-time computer vision systems for images, videos, and live camera feeds using the latest YOLOv8 and YOLOv11 models.
What I offer:
- Custom object detection using YOLO (v5, v8, v11)
- Real-time object tracking with DeepSORT and ByteTrack
- Image classification and segmentation models
- Object counting and crowd counting systems
- CCTV and surveillance AI integration
- REST API deployment using FastAPI and Flask
- Defect detection for manufacturing and quality control
- Model optimization with ONNX and TensorRT
Why choose me:
- 5+ years building production computer vision systems
- Models trained on custom datasets for your exact use case
- Fast delivery with clean, well-commented Python code
- Works on images, video files, and live webcam or CCTV feeds
- Free revision until your model performs accurately
Message me before ordering so I can review your dataset and confirm delivery time.
Let's build your vision AI system today.
Programming language:
Python
•
SQL
•
Colab
•
MLflow
•
Amazon SageMaker
Tools:
OpenCV
•
OpenNN
•
TensorFlow
•
MLflow
•
SimpleCV
•
CVAT
•
Colab
Frameworks:
Scikit-learn
•
DeepPy
•
Google ML Kit
•
SimpleCV
•
PyTorch
FAQ
Can you train the model to detect objects specific to my industry, like machinery parts or crops?
Yes. I train custom YOLO models on your own labelled dataset or help you build one from scratch. Whether it's factory parts, agricultural produce, or branded products, the model learns your specific objects.
Will the detection system work on a Raspberry Pi or edge device, not just a cloud server?
Yes. I optimize models using ONNX and TensorRT so they run efficiently on edge hardware including Raspberry Pi, NVIDIA Jetson Nano, and similar devices without needing cloud connectivity.
What happens if my dataset is too small to train accurately?
I apply data augmentation, transfer learning, and synthetic data techniques to boost model accuracy even on small datasets of 100–500 images, which is enough for most custom detection tasks.
