Computer Vision Development

Custom object detection models trained on your specific objects and environments, product defects, PPE compliance, vehicle types, inventory items, or any visually defined category that matters to your operation. Model architecture selected based on throughput and accuracy requirements: YOLOv8 or RT-DETR for real-time detection at 30-60 FPS on edge hardware, Detectron2 (Mask R-CNN) for instance segmentation where per-object pixel masks are needed, and MediaPipe for human pose and hand keypoint detection in interaction applications. Transfer learning from ImageNet or COCO pre-trained weights reduces the labelled data requirement, a well-scoped detection task can reach production-grade accuracy with 500-2,000 labelled images per class rather than tens of thousands. Data augmentation pipeline (random flip, rotation, brightness and contrast variation, synthetic occlusion) applied during training to make the model resilient to real-world variability in lighting, angle, and partial obstruction. Evaluation framework covering precision, recall, F1, and mAP at multiple IoU thresholds, measured on a held-out test set drawn from your actual production environment, not the academic benchmarks that make models look better than they perform in your facility. False positive and false negative rate analysed separately because the cost of each differs by application: a safety system would rather flag a false alarm than miss a genuine hazard; a defect rejection system prefers the reverse.

Automated visual inspection for manufacturing, food processing, pharmaceutical, and electronics production lines, replacing or augmenting manual visual inspection that creates throughput bottlenecks and produces inconsistent results across shifts and fatigue levels. Defect detection models trained on your specific defect taxonomy: surface scratches, coating voids, colour deviations, contamination, dimensional deviations from nominal, assembly errors, and missing components, each treated as a separate detection class with its own precision/recall operating point. Anomaly detection approach (autoencoders, PatchCore, FastFlow) used when labelled defect examples are too few to train a supervised model: the model learns what a good part looks like and flags deviations from that baseline, requiring only good-part images for training. Line speed requirements determine the processing architecture: a 100ms latency budget allows cloud processing for slow-moving products; a 10-30ms budget requires on-camera or edge GPU inference. Integration with production line control systems via OPC-UA or Modbus to trigger ejection, lane diversion, or an alert to the line operator when a reject is detected. Confidence threshold calibration to your specific quality standard: a stricter threshold on a pharmaceutical line than on a consumer packaging line, with the threshold reviewed against production data after deployment. Batch traceability log of inspection results linked to production order and timestamp for quality audit purposes.

Optical character recognition and structured data extraction from invoices, purchase orders, contracts, medical forms, ID documents, handwritten records, and any paper or PDF document your workflow currently requires a human to read and key into a system. Layout analysis using document understanding models (Azure Document Intelligence, AWS Textract, or open-source alternatives like LayoutLMv3 and PaddleOCR) to understand document structure before field extraction, treating an invoice's header, line items, and footer as semantically distinct zones rather than a flat stream of text. Field-level extraction with confidence scores: each extracted value is returned with a probability estimate so low-confidence extractions are routed to a human review queue rather than written directly to the downstream system. Validation against expected formats and business rules: an invoice total must match the sum of line items within tolerance, a date field must parse to a valid calendar date, a VAT number must match country-specific checksum rules. Multi-template handling without per-template configuration: document understanding models trained on diverse document layouts generalise to templates they haven't seen in training, unlike traditional template-matching OCR that requires per-supplier configuration. Downstream integration to push extracted structured data to your ERP (SAP, Oracle, NetSuite via API), CRM, or workflow system, with error handling and retry logic for failed pushes. For high-volume document processing, OpenAI Batch API or asynchronous processing queues reduce per-document cost by 50% compared to synchronous API calls.

Video analytics systems that extract structured operational data from camera feeds without requiring a human to monitor every feed in real time. People counting and traffic flow analysis for retail: entry counts, dwell time by zone, queue length at checkout, and peak hour traffic patterns, feeding demand-based staffing models and store layout optimisation. Occupancy monitoring for facilities management: space utilisation by area, time-of-day patterns, and occupancy threshold alerts for fire safety compliance. PPE compliance monitoring for industrial sites: detection of hard hat, hi-vis vest, and safety boot presence in defined zones, with alerting on non-compliance and a clip saved for review. Video processing pipeline architecture: RTSP stream ingestion from IP cameras, frame sampling at the detection rate required (typically 1-5 FPS for analytics, not 30 FPS for cost efficiency), and a detection and tracking model (ByteTrack or DeepSORT for multi-object tracking across frames). Results feed event aggregation to time-series metrics and storage of tagged clips (30-second window around the detection event) for human review of flagged incidents. Deployment on-premises via edge server or in cloud via GPU-enabled instance, with the decision driven by data sovereignty requirements and the number of concurrent camera feeds. Dashboard displaying real-time metrics and historical trend data, with webhook-based alerting for defined threshold conditions.

LLM vision models (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro) handle visual tasks that require language understanding alongside image analysis, and where the variability of inputs makes training a custom model impractical or prohibitively data-intensive. Document Q&A: a user uploads a contract, financial statement, or technical drawing and asks natural language questions about its content, the vision model reads the layout and text together, handling tables, annotations, and handwritten notes that pure OCR pipelines misinterpret. Image description and accessibility captioning: generating alt text for product images, describing complex charts and diagrams for screen readers, or producing structured summaries of medical imaging reports for non-specialist audiences. Multi-modal data extraction: extracting structured fields from scanned forms, utility bills, or insurance documents where layout variability is too high for template-based extraction but low enough that a vision LLM can reason about field locations reliably. Prompt engineering for production use: system prompt design that constrains output format (JSON schema), handles edge cases (image too dark, page rotated, partial document), and produces consistent structured output across diverse inputs. Output validation against expected schemas before downstream use, with fallback handling for malformed responses. Cost optimisation: image resizing to the minimum resolution that preserves extraction accuracy, caching of repeated extractions (same document, multiple queries), and batch processing where latency tolerance allows.

Edge deployment runs the inference model on hardware local to the camera or production line, eliminating cloud round-trip latency, removing cloud connectivity as a single point of failure, and keeping sensitive visual data on-site for privacy or regulatory compliance. Hardware selection based on inference requirements: NVIDIA Jetson AGX Orin for complex models requiring up to 200 TOPS (object detection at 60 FPS, multi-camera processing), Jetson Orin NX or Nano for lighter models where cost-per-node matters, Intel Core i7/i9 NUC with Intel Arc GPU for environments where NVIDIA hardware is restricted, and Hailo-8 AI accelerator for ultra-low-power deployments. Model optimisation for edge hardware: TensorRT quantisation (INT8 or FP16) reducing model size by 3-4x with less than 1-2% accuracy loss on typical detection tasks, ONNX export for hardware-agnostic deployment, and TFLite for ARM-based edge devices. OTA model update capability: models are containerised (Docker on Jetson with NVIDIA Container Toolkit) and updates pushed from a central model registry, so deploying a retrained model to 50 edge devices across a facility does not require physical access to each device. Offline operation with local result storage: when cloud connectivity is unavailable, inference results are stored locally and synchronised to the central data layer on reconnection, with duplicate detection to prevent double-counting. Deployment playbooks validated in manufacturing, retail, construction site monitoring, and agricultural inspection environments.