Computer Vision Solutions
End-to-end computer vision systems for object detection, image classification, and real-time video analytics. Applicable to quality control in manufacturing, retail shelf analysis, security monitoring, and document processing. We train, optimize, and deploy models to cloud or edge devices.
Tech Stack
Key Features
- Custom object detection models trained on your domain data
- Image and video classification with 90%+ accuracy
- Real-time inference optimized for edge devices and mobile
- Integration with CCTV systems, manufacturing lines, or apps
- Defect detection and automated quality-control pipelines
- On-premise and cloud deployment with monitoring dashboards
Service Level
Frequently Asked Questions
How much training data is needed for a custom computer vision model?
For object detection (e.g., defect detection on a production line), we recommend a minimum of 500 labelled images per class for initial model training, with 1,000–2,000 per class for production-grade performance. For image classification tasks, 200–500 examples per class can be sufficient with transfer learning from a pre-trained backbone. We provide data labelling guidance and can assist with annotation using tools like Label Studio or Roboflow.
What accuracy levels can we expect from a custom vision model?
For well-defined defect detection tasks with sufficient labelled data, we typically achieve 90–97% accuracy (measured as precision and recall on a held-out test set). Accuracy depends on: visual consistency of the defects, image quality and lighting conditions, and diversity of the training set. We always benchmark against your current manual inspection accuracy to calculate the improvement and calculate the false-positive rate, which matters more than raw accuracy in most QA applications.
Can the model run on our existing cameras and hardware?
Yes. We optimise models for the hardware you already have. For NVIDIA GPU-equipped edge devices (Jetson Nano, Jetson Orin), we export models to TensorRT for 3–10× inference speedup. For CPU-only environments, we use ONNX Runtime. For cloud deployment (AWS, GCP), we containerise with Docker and expose a REST API. If your cameras output standard RTSP streams, we can connect directly. We assess your hardware constraints during scoping and design the inference pipeline accordingly.
How does the vision system integrate with our production line or CCTV?
Integration depends on your camera infrastructure. For IP cameras with RTSP streams, we connect OpenCV directly to the stream and run inference in real time. For CCTV DVR/NVR systems, we extract frames at a configurable rate (typically 1–5 fps for QA applications). For PLC-integrated manufacturing lines, we output inspection results as a signal (pass/fail) via Modbus TCP or MQTT, which your existing PLC can consume without modification. We handle the integration layer so your hardware team doesn't need to understand the ML pipeline.
What happens when the model encounters an image it hasn't seen before?
Every production vision model we deploy includes a confidence threshold: if the model's prediction confidence falls below the threshold (typically 0.7–0.85), the image is flagged for human review rather than acted upon automatically. This prevents low-confidence false positives from affecting your production process. Flagged images are logged and periodically used to expand the training set, improving the model over time. We include monitoring dashboards that show confidence distribution so you can detect when the model is encountering new edge cases.
Ready to Get Started?
Let's discuss your project requirements and how we can help you achieve your goals with our computer vision solutions expertise.