Computer Vision Agency

Where does the vision model run?

Depending on your power, latency and hardware budget, we run inference directly on the device or on your server:

• NVIDIA Jetson Orin Nano (7–25 W): multi-stream video, full PyTorch or ONNX Runtime pipelines, and larger vision models.
• Raspberry Pi 5 with a Hailo-8L accelerator (~1.5 W): real-time YOLO object detection at the camera without a data center round trip.
• Phone camera + FastAPI backend: the mobile app captures and preprocesses frames while a cloud or on-premise server returns classifications and logs telemetry.

We compare the edge board trade-offs in ESP32-S3 vs Jetson Orin Nano vs Hailo-8L.

What if we don't have a labeled dataset yet?

Most custom computer vision development projects start with raw camera footage or a few sample parts rather than a clean dataset. We set up the capture protocol, annotate the initial batch in Roboflow, and build a semi-automatic labeling pipeline as the base model improves so your team isn't stuck labeling tens of thousands of frames by hand.

How do you handle motion blur, glare and tight crops?

A model is only as reliable as the frame fed into it. Before a frame reaches the detector, we write the OpenCV and geometric preprocessing around it:

• Motion-stability gating: when a camera is mounted on a moving robot, we check IMU or kinematic angular rates before triggering capture so motion blur doesn't ruin small-object detection.
• Marker and ROI alignment: we use fiducial markers and geometric transforms to mask, rotate and crop only the target surface into the model's native input resolution.
• Two-stage boundary filtering: we run inference with a wider context margin and filter detections against a tighter inner boundary to reject edge reflections and false positives.

What computer vision work have you built?

• JAXA-NASA Kibo Robot Programming Challenge (4th worldwide): we built the onboard vision and autonomous navigation pipeline for NASA's Astrobee robot on the International Space Station, turning a 1280×720 camera feed into ArUco-aligned 480×480 crops for landmark and target detection on legacy Android 7.1.1 hardware. Read the technical breakdown in Our Journey to the 6th Kibo-RPC International Round.
• Diet Engine (published on ScienceDirect): a real-time food recognition phone app using YOLOv8 and deep CNNs that identifies meals from a smartphone photo with 86% classification accuracy and estimates macro and micronutrient breakdowns. See Diet Engine.
• CORTEX-Health: quantized medical imaging models for edge deployment where INT8 optimization reduced inference time by 40% while preserving clinical diagnostic accuracy.

Can we hire you for just one part of the pipeline?

Yes. Some clients use us as their full computer vision development firm from camera selection to PCB, firmware and mobile app. Others bring a trained PyTorch model that runs too slowly and only need us to quantize it and deploy it onto a Jetson or Raspberry Pi. We scope only the part you need.