Computer Vision Development Services

Detection and classification of objects, defects, or anomalies in images and video using YOLO variants (YOLOv8, YOLOv11), EfficientDet, or Vision Transformer architectures depending on accuracy vs. latency requirements. Training on your annotated dataset using transfer learning from ImageNet or COCO pre-trained weights, fine-tuned on your specific product types, defect categories, and environmental conditions. Real-time inference deployed as a REST API on GPU-backed infrastructure (AWS, GCP) or on-device via ONNX or TensorFlow Lite for edge deployment. Confidence scoring with configurable thresholds: below-threshold detections route to a human review queue with the image and bounding box overlay rather than producing a confident wrong answer. We've processed thousands of industrial document images per day through OCR and detection pipelines in production environments with variable lighting and scan quality.

Structured data extraction from documents that don't conform to a fixed template, invoices from 50+ different suppliers, medical forms in regional variants, shipping labels from multiple carriers, insurance certificates in different formats. Layout-aware models (LayoutLM, Donut, Azure Document Intelligence) understand document structure rather than just reading text left-to-right, correctly associating labels with values across multi-column layouts. Pre-processing pipeline handles the scan quality issues that break naive OCR: deskewing documents scanned at an angle, contrast normalization for faded originals, noise removal from fax artifacts. Confidence scoring at field level: each extracted field gets a confidence score, and fields below threshold are highlighted in the human review interface rather than the whole document being rejected. We've shipped industrial OCR systems processing thousands of documents per month with 95%+ straight-through processing rates.

Automated visual quality control for manufacturing lines where manual inspection is a bottleneck or accuracy varies by inspector fatigue. Defect detection models trained on your specific product types and defect categories, surface scratches, dimensional variation, color deviation, assembly errors, foreign material, using examples from your actual production rejects, not synthetic data that doesn't match your conditions.

Model architecture is selected based on the inspection task: YOLOv8 or Detectron2 for multi-class defect localisation, EfficientNet for binary pass/fail classification on uniform products. Inference runs through ONNX Runtime or TensorRT-optimised engines, achieving sub-100ms latency on NVIDIA Jetson edge devices mounted at the inspection station without a round-trip to a central server. Camera integration covers GigE Vision and USB3 Vision industrial cameras with ISP-level pre-processing (white balance, gain, sharpening) applied in the GStreamer pipeline before frames reach the inference stage. mAP (mean Average Precision) and precision-recall curves at each confidence threshold are reported during model evaluation so you understand the trade-off between missed defects and false rejects at your specific operating point.

Pass/fail output with defect type, bounding box, and confidence score overlaid on the image feeds the operator interface. Integration with your MES or production control system handles automated divert, reject, or hold routing without operator intervention. False negative rate (missed defects) is the critical metric we optimize against, a detection system that misses 2% of defects is not the same as one that misses 0.1%.

Analysis of video streams for operational intelligence: people counting and zone occupancy for retail and facilities management, vehicle detection and classification for parking and logistics, queue length estimation for service operations, and behavior recognition for security and compliance monitoring. Object tracking across frames using Deep SORT or ByteTrack associates detections across time, the difference between a raw "person detected" event and understanding that the same individual spent 8 minutes in a zone. Processing of both live RTSP feeds and recorded footage via a batch processing API. Alert events (occupancy threshold exceeded, perimeter breach, queue backup beyond SLA) delivered via webhook, Slack, or a dashboard alert. Structured event data, timestamped, categorized, with frame references, integrates with your operations platform rather than requiring staff to review footage manually.

Computer vision systems for clinical and healthcare applications built with the accuracy and validation requirements that medical use cases demand. Classification models for X-ray and scan findings, segmentation models that delineate regions of interest for clinical review, and anomaly detection for continuous patient monitoring video streams.

Model architecture choices reflect clinical requirements: U-Net variants for pixel-level organ and lesion segmentation, ResNet-50 or EfficientNet for multi-label classification of radiograph findings, and LSTM-based temporal models for deterioration detection in continuous monitoring feeds. Inference pipelines are designed for DICOM input, with GDCM-based pre-processing normalising pixel spacing, window-level settings, and orientation before the image reaches the model. Model validation is conducted against ground-truth datasets labelled by clinical experts with inter-rater agreement measured by Cohen's kappa, and sensitivity/specificity metrics are reported at the clinical decision threshold, not averaged across a ROC curve. A system with 98% AUC that misclassifies a specific abnormality class is not clinically useful; we report performance at the threshold your clinical team will operate at.

Integration with clinical information systems for structured output delivery: findings exported as HL7 FHIR DiagnosticReport resources or structured PDF reports rather than free-text that requires re-interpretation. We've built AI systems for remote patient monitoring that reduced clinical decision latency by 20% and flagged deterioration events earlier than manual observation, deployed in ICU monitoring workflows.

Custom model training on your domain-specific data when pre-trained models don't reach the accuracy threshold your use case requires, because a model trained on ImageNet hasn't seen your specific product defects, your document layouts, or your clinical images. Data collection strategy to identify and fill the gaps in your training set (the defect categories with too few examples, the edge case conditions that were underrepresented). Annotation pipeline setup using Label Studio or Roboflow with inter-annotator agreement metrics to ensure label consistency. Training infrastructure on GPU instances (A100 or H100) with experiment tracking in MLflow or Weights & Biases. Transfer learning from ViT, ResNet, or EfficientNet foundation models reduces the labelled data requirement by 60-80% compared to training from scratch. Ongoing model improvement as new production examples become available, the model gets more accurate as it sees more of your real-world data.