We build computer vision systems that extract actionable insight from images and video accurate, reliable, and built for production, not just demos.
PyTorch • TensorFlow • YOLO • OpenCV • Mask R-CNN • Vision Transformer
Computer Vision
Computer Vision Services
What We Do
Computer vision enables machines to interpret and understand visual information identifying objects, classifying scenes, tracking movement, reading text, detecting anomalies, and segmenting images at a speed and scale no human operator can match.
We build vision systems across the full development lifecycle data labelling, model training, deployment, integration, and performance monitoring. We work with the tools that are right for your use case and are honest about what your data can support.
Our Computer Vision Services
Data Labelling
Accurate, consistent labelling is the foundation of any reliable vision model. We provide specialist annotation using bounding boxes, polygons, segmentation masks, keypoints, cuboids, and classification labels with quality assurance built in.
Bounding box, polygon, and semantic segmentation annotation
Keypoint and pose estimation labelling
3D cuboid annotation for depth and spatial data
Quality assurance and inter-annotator agreement review
Object Detection
We build detection systems that identify and locate objects in images and video using YOLO, Faster R-CNN, and EfficientDet based on your accuracy and latency requirements.
Real-time object detection in live video streams
Defect and anomaly detection in manufacturing
Person detection for safety and security applications
Product and barcode detection for retail and logistics
Object Tracking
Single and multi-object tracking systems that follow targets across video frames recording trajectories, maintaining identity, and detecting zone events.
Multi-object tracking with identity persistence
Zone-based event detection and triggering
Player and ball tracking for sports analytics
Vehicle and pedestrian tracking for traffic applications
Image Classification
CNN and transfer learning models that assign images to categories sorting products, identifying conditions in medical scans, classifying document types, and routing visual inputs in automated workflows.
Binary and multi-class classification
Transfer learning from ResNet, EfficientNet, ViT
Medical image classification for diagnosis support
Product image categorisation at scale
Image Segmentation
Pixel-level classification for precise delineation of objects, regions, and boundaries semantic, instance, and panoptic segmentation for medical imaging, industrial QC, and autonomous perception.
Semantic segmentation using DeepLab and FCN
Instance segmentation using Mask R-CNN
Medical image segmentation for tissue and organ delineation
Satellite and aerial image segmentation
Intelligent Video Analysis
Real-time and batch video processing that extracts structured metadata detecting objects, recognising actions, identifying events, and triggering alerts from live camera feeds or recorded footage.
Action recognition and behaviour detection
Crowd density and movement analytics
Anomaly detection in CCTV and industrial camera feeds
Integration with existing camera infrastructure and VMS systems
Optical Character Recognition (OCR)
OCR systems that convert printed and handwritten text in images into machine-readable data with NLP post-processing for correction and entity extraction.
Invoice, form, and document OCR with structured field extraction
Product label and packaging text recognition
Multi-language OCR for international content
Integration with document management and ERP systems
Visual Search
Content-based image retrieval systems that let users search by photograph reverse image search, product similarity matching, and visual recommendation engines tailored to your catalogue.
Reverse image search for product discovery
Similarity search using embeddings and vector databases
Augmented reality try-on and overlay search
Image deduplication and near-duplicate detection
Industry Applications
Transportation
Pedestrian and vehicle detection, parking occupancy, traffic flow analysis, road condition monitoring, and licence plate recognition.
Retail & E-commerce
Footfall analysis, visual search, inventory inspection, counterfeit detection, and self-checkout automation.
Healthcare
X-ray, CT, and MRI analysis, digital pathology, patient identification, wearable integration, and rehabilitation monitoring.
Manufacturing
Automated defect detection, barcode and QR reading, predictive maintenance via visual monitoring, and safety compliance monitoring.
Education
Attendance tracking, learning engagement monitoring, campus safety, intrusion detection, and movement analysis.
Sports & Fitness
Player and ball tracking, real-time performance analysis, movement coaching feedback, and game analytics.
Smart Cities
Perimeter monitoring, weapon and threat detection, hygiene compliance, and traffic management from surveillance infrastructure.
Technology Stack
Languages
Python • Java • C++ • C • R
Vision Libraries
OpenCV • Dlib • Albumentations • SimpleCV • Mahotas
Deep Learning
PyTorch • TensorFlow • Keras • ONNX • Fast.ai • Caffe
Cloud AI Platforms
AWS SageMaker & Rekognition • Google Cloud AI • Azure Computer Vision
Vector Search
FAISS • Pinecone • Weaviate
Architectures
YOLO • Faster R-CNN • Mask R-CNN • ResNet • EfficientNet • Vision Transformer (ViT) • CNN • LSTM • GAN
Why Web Chip Armor
Industry-specific understanding
A vision system for healthcare imaging has completely different accuracy, privacy, and latency requirements from one monitoring a retail floor. We bring genuine domain understanding to every project.
Full pipeline capability
Data labelling, model training, optimisation, deployment, and integration we cover everything a production-grade vision system requires, not just one part of it.
You own everything
All code, models, and documentation are transferred to you on project completion. No artificial dependencies on us continuing to be involved unless you choose ongoing support.
Real-time and batch both
We build live streaming systems with low-latency inference and batch processing pipelines for recorded data. The architecture is chosen based on your actual performance requirements.
Data privacy built in
Facial images, medical imagery, proprietary product visuals sensitive data is handled with encryption, access controls, data minimisation, and GDPR-compliant workflows from day one.
Agile delivery with regular visibility
Structured sprints, working models at every stage, and clear communication throughout. You never receive a final system months later with no intermediate checkpoints.
Frequently Asked Questions
How much data do we need to build a computer vision model?
It depends on the task complexity. Simple binary classification using transfer learning can work with a few hundred labelled images. Object detection and segmentation typically need several thousand annotated examples. We assess your data at the start and advise honestly on whether it’s sufficient or what augmentation strategies could help.
Can your systems work in real time on live camera feeds?
Yes. We build both real-time streaming systems and batch processing pipelines. Real-time systems require careful model optimisation, hardware selection, and deployment engineering to meet latency targets. We design for your specific performance requirements from the outset not as an afterthought.
Can you integrate with our existing cameras and systems?
Yes. Integration with existing cameras, ERP platforms, IoT infrastructure, and business applications is a core part of every deployment. We connect vision system outputs to downstream processes via REST APIs, database writes, message queues, or direct application integration planned carefully before development begins to avoid rework.
How do you handle sensitive visual data like facial images or medical imagery?
Encryption in transit and at rest, strict access controls, audit logging, and data minimisation. For facial recognition we advise on the applicable legal and ethical framework before development begins. For medical imagery we apply clinical data governance standards. NDAs are signed before any project discussion starts.
Do you provide support after deployment?
Yes. Monitoring accuracy, detecting performance degradation as real-world data shifts, retraining on new data, and extending capabilities as requirements evolve. Vision systems degrade as the visual environment changes we prevent that through structured post-deployment maintenance.
What engagement models do you offer?
Dedicated team for sustained vision development needs, multiple models, or continuous improvement programmes. Fixed-price project for well-scoped deliverables like a specific detection system, OCR pipeline, or visual search model. We advise on which fits your situation after a discovery conversation.