Computer Vision

AI That Sees, Understands, and Acts on the World

Computer Vision gives machines the ability to interpret and understand visual data — from a single image to a live video stream. We build CV systems that don't just detect pixels — they extract business intelligence from everything your cameras see.

Face Detection & Recognition

Identify, verify, and authenticate individuals at scale

Object Detection

Locate and classify multiple objects in real time

Image Classification

Categorise images at production scale with high accuracy

Video Analytics

Extract intelligence from live and recorded video streams
$48.6B
Computer Vision market size by 2030
Grand View Research 2025
99.7%
accuracy achievable on controlled face recognition tasks
NIST Face Recognition Vendor Test
30ms
real-time inference latency with optimised CV models
NVIDIA TensorRT Benchmarks
80%
reduction in manual visual inspection costs with AI CV
Deloitte Manufacturing Study
Why Custom Models

Generic AI Is a Starting Point. Custom AI Is a Competitive Advantage.

GPT-4, Claude, Gemini — these are exceptional foundation models. But they were trained on the internet, not your industry. They don’t know your product catalog, your compliance requirements, your customer terminology, or your internal processes.

A custom-trained or fine-tuned model does. It speaks your language, stays within your guardrails, costs a fraction to run at scale, and doesn’t share your data with anyone. That’s not just better performance — it’s a durable strategic moat your competitors can’t easily replicate.

We handle the full lifecycle: data preparation, model selection, training or fine-tuning, evaluation, and production deployment — so your team gets the AI capability without the AI research team headcount.

  • Trained on your real-world data — not stock datasets
  • Deployable on cloud, edge devices, or on-premise servers
  • Integrated into your existing cameras & infrastructure
  • Continuously improved with production feedback loops
Where CV Creates Business Value
Manufacturing QA
Detect defects, measure tolerances, and flag anomalies on production lines faster and more accurately than human inspection.
Retail Analytics
Track footfall, measure shelf compliance, analyse customer behaviour, and prevent loss — all from existing CCTV infrastructure.
Access Control
Frictionless face-based authentication for secure facilities, offices, and restricted zones — replacing badges and PINs.
Traffic & Fleet
Vehicle counting, licence plate recognition, driver behaviour monitoring, and route optimisation from dashcam or roadside feeds.
Medical Imaging
Assist radiologists with scan analysis, anomaly detection in X-rays and MRIs, and pathology slide classification at scale.
Logistics & Warehousing
Barcode-free item identification, damage detection on inbound goods, and pick-and-place automation for fulfilment operations.
What We Build

Four Computer Vision
Capabilities, Production-Ready

Each capability is a complete service — from model training on your data to
deployment in your environment with monitoring in place.
Face Detection & Recognition
01
Identify and Verify Individuals With Precision — at Any Scale
We build face detection and recognition systems that go beyond simple presence detection. Our models locate faces in complex scenes, recognise individuals across varying lighting and angles, verify identity for access control, and flag unknown faces in restricted environments — all in real time.
Capabilities
  • Real-time face detection in crowds & video
  • 1:1 face verification (authentication)
  • 1:N face identification (recognition from database)
  • Liveness detection (anti-spoofing)
  • Emotion & age/gender attribute analysis
Use Cases
  • Secure facility access control
  • Employee time & attendance systems
  • Customer identification in retail & banking
  • KYC & identity verification workflows
  • Crowd safety & VIP recognition
Object Detection
02
Locate, Classify and Track Multiple Objects in Real Time
Object detection goes beyond identifying what's in an image — it tells you exactly where each object is, what it is, how many there are, and in video, how they're moving. We train custom detection models on the specific objects, environments, and edge cases that matter to your business.
Capabilities
  • Multi-class object detection & localisation
  • Real-time object tracking across video frames
  • Small object & occluded object detection
  • Custom class training on proprietary datasets
  • Edge deployment for low-latency inference
Use Cases
  • Manufacturing defect & anomaly detection
  • Retail shelf occupancy & planogram compliance
  • PPE & safety equipment compliance monitoring
  • Warehouse inventory tracking & picking assist
  • Traffic counting & vehicle classification
Image Classification
03
Categorise Any Image Instantly — Trained on Your Categories, Your Data
Image classification assigns one or more labels to an entire image — telling you what type of thing is present, not just where it is. We build classification models for complex, domain-specific taxonomies where generic models fail completely, using your labelled data to achieve precision generic models can't match.
Capabilities
  • Single & multi-label image classification
  • Fine-grained visual categorisation
  • High-throughput batch classification pipelines
  • Confidence scoring & uncertainty quantification
  • Active learning for continuous improvement
Use Cases
  • Product image categorisation for eCommerce
  • Medical scan & pathology classification
  • Document & form type classification
  • Satellite & aerial image analysis
  • Content moderation & brand safety
Video Analytics
04
Turn Your Camera Feeds Into a Continuous Stream of Business Intelligence
Video analytics combines detection, tracking, and temporal reasoning to understand events as they unfold over time. We build systems that monitor live and recorded footage at scale — extracting patterns, flagging incidents, measuring behaviour, and generating automated reports from your existing camera infrastructure.
Capabilities
  • Behaviour & activity recognition
  • Zone monitoring & intrusion detection
  • People & vehicle counting & flow analysis
  • Anomaly & incident detection with alerts
  • Multi-camera tracking & scene understanding
Use Cases
  • Retail footfall & customer journey analytics
  • Workplace safety monitoring & incident logging
  • Smart city traffic & pedestrian analysis
  • Perimeter & critical infrastructure security
  • Sports & event performance analysis
Our Process

From Camera Feed to Deployed
Vision System

A field-tested delivery process that accounts for the real-world messiness of CV projects —
varying lighting, hardware constraints, and edge cases.
01
Phase 1
Site Survey & Data Collection
Computer Vision lives or dies by the quality and diversity of its training data. We survey your physical environment — camera placement, lighting conditions, subject variability — and design a data collection strategy that captures the full range of conditions your model will encounter in production.
Activities
  • Physical environment & camera audit
  • Lighting, angle & occlusion analysis
  • Edge case identification & scenario mapping
  • Data collection protocol design
Deliverables
  • Environment assessment report
  • Data collection plan & labelling guide
  • Hardware recommendations (if needed)
  • Dataset size & diversity targets
02
Phase 2
Data Labelling & Augmentation
Quality labels are the most critical input to a CV model. We run structured annotation workflows — bounding boxes, segmentation masks, keypoints, classification labels — with quality control passes to catch labelling errors before they corrupt training. We also apply targeted augmentation to expand dataset diversity.
Activities
  • Annotation workflow setup (CVAT, Label Studio)
  • Multi-pass quality control & review
  • Data augmentation strategy (flips, crops, lighting)
  • Train / validation / test split
Deliverables
  • Annotated dataset in target format (COCO, YOLO)
  • Labelling quality assurance report
  • Augmented dataset pipeline code
  • Dataset statistics & coverage report
03
Phase 3
Model Training & Optimisation
We select the right architecture for your task and deployment constraints — from lightweight MobileNet for edge devices to YOLOv11 for real-time detection to ViT for classification — and train with iterative evaluation cycles. Model optimisation (quantisation, pruning, TensorRT conversion) ensures production performance targets are met.
Activities
  • Architecture selection & baseline benchmarking
  • Transfer learning from pre-trained weights
  • Hyperparameter tuning & ablation studies
  • Quantisation & pruning for edge deployment
Deliverables
  • Trained model checkpoints
  • Model card (accuracy, speed, size, limitations)
  • Training metrics & experiment logs
  • Optimised inference model (ONNX / TensorRT)
04
Phase 4
Deployment & Integration
We deploy the vision system into your environment — connecting to your camera hardware, integrating inference results into your downstream systems (alerts, dashboards, databases), and setting up monitoring for model performance, system health, and data drift over time.
Activities
  • Camera stream integration & RTSP pipeline setup
  • API or webhook output integration
  • Dashboard & alerting configuration
  • Performance monitoring & drift detection
Deliverables
  • Live production CV system
  • Integration documentation & API specs
  • Monitoring dashboard & alert setup
  • Retraining & maintenance runbook
Technology Stack

The Frameworks & Tools
We Build With

We work across the full CV stack — from annotation tooling and training
frameworks to optimised inference engines and edge deployment platforms.
Face & Detection
  • YOLOv8 / YOLOv11 (detection)
  • DeepFace / InsightFace
  • MediaPipe (face mesh & pose)
  • OpenCV (preprocessing)
  • MTCNN (face detection)
  • FaceNet / ArcFace (recognition)
Object Detection
  • YOLOv8 / YOLOv11
  • Detectron2 (Facebook AI)
  • RT-DETR (real-time transformer)
  • Roboflow (dataset management)
  • SAM (Segment Anything Model)
  • CVAT / Label Studio (annotation)
Classification
  • Vision Transformer (ViT)
  • EfficientNet / ConvNeXt
  • ResNet / DenseNet fine-tuning
  • CLIP (zero-shot classification)
  • PyTorch / TensorFlow
  • Hugging Face Transformers
Video & Deployment
  • NVIDIA DeepStream (video analytics)
  • TensorRT (inference optimisation)
  • ONNX Runtime (cross-platform)
  • OpenVINO (Intel edge)
  • NVIDIA Jetson (edge hardware)
  • AWS Rekognition / GCP Vision API
Industry Applications

Computer Vision Across
Every Sector

CV is one of the most broadly applicable AI disciplines — here are
the sectors where we deliver the most impact.
Manufacturing & Quality Control
Automated visual inspection for defect detection, dimensional measurement, assembly verification, and surface quality grading — running 24/7 on production lines without fatigue.
Object Detection
Classification
Retail & eCommerce
Shelf compliance monitoring, customer journey analysis, queue management, product image classification for catalogues, and loss prevention through behaviour detection.
Video Analytics
Classification
Security & Access Control
Face-based facility access, perimeter intrusion detection, unattended object alerts, crowd monitoring, and behaviour anomaly detection for critical infrastructure and campuses.
Face Recognition
Video Analytics
Healthcare & Medical Imaging
Radiology assist for X-ray, MRI and CT scan analysis, pathology slide classification, wound assessment imaging, and surgical tool tracking in operating rooms.
Classification
Object Detection
Automotive & Transportation
Licence plate recognition, driver behaviour monitoring, dashcam incident detection, vehicle classification for tolling, and traffic flow optimisation systems.
Object Detection
Video Analytics
Logistics & Warehousing
Barcode-free item scanning, inbound damage detection, picking accuracy verification, dock door monitoring, and forklift safety zone compliance from warehouse cameras.
Object Detection
Video Analytics
The Impact

Computer Vision Delivers Measurable, Rapid ROI

CV systems pay for themselves quickly — particularly in quality control, security, and logistics, where the cost of human error and manual oversight is directly measurable. The consistency and speed of machine vision simply can't be matched at scale.
Sample ROI — Manufacturing Quality Control
  • Current manual inspectors (FTE)8 inspectors
  • Annual labour cost$320,000
  • Current defect escape rate2.4% of output
  • CV system deployment cost$45,000–$80,000
  • Annual system operating cost$12,000
  • Year-1 net saving$228,000+
* Illustrative example based on average manufacturing client outcomes. Actual figures vary by output volume, defect rate, and inspection complexity. We model your specific ROI during discovery.
Why The OrangeByte

CV Engineers Who've Shipped
Systems in the Real World

Computer Vision in the lab is easy. CV in production — with variable lighting,
dirty lenses, edge cases, and 24/7 uptime requirements — is where
experience matters most.
Real-World Environment First
We design for your actual conditions — not controlled lab settings. That means hardware audits, edge case cataloguing, and testing under adversarial conditions before deployment.
Edge & Cloud Deployment
We deploy on NVIDIA Jetson edge devices, on-premise servers, and cloud infrastructure — optimising for latency, privacy, and cost depending on your requirements.
End-to-End Data Ownership
We build and hand over the annotation pipelines, training code, and model weights — so you own everything and can retrain with new data internally as your environment evolves.
Camera-Agnostic Integration
We integrate with your existing camera infrastructure — IP cameras, RTSP streams, USB cameras, industrial line-scan cameras — without requiring you to replace hardware.
Privacy & Compliance by Design
For face recognition and surveillance use cases, we architect with privacy in mind — on-premise processing, data minimisation, consent management, and GDPR-aligned retention policies.
Active Learning & Continuous Improvement
Production CV systems encounter new scenarios over time. We build active learning pipelines that flag uncertain predictions for review, feeding new labelled data back into retraining.
Ready to See What Your Cameras Are Missing?
Book a free Computer Vision Discovery Call. We'll assess your environment, identify your highest-impact CV use case, and scope a practical path to deployment.
LET'S TALK

Not Sure Which CV Capability Fits Your Problem?

Describe what you're trying to detect, classify, or monitor — and we'll tell you which approach,
what hardware, and what timeline is realistic for your use case.