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Computer Vision

Custom Computer Vision for Business

We develop computer vision systems that see, understand, and act. Automated quality control, document processing, real-time video analytics, and object detection for any industry.

Computer vision

Machines that see and understand the visual world

Computer vision enables machines to interpret images and video with superhuman precision. From detecting microscopic defects on a production line to counting people in a retail space in real time, computer vision transforms visual data into automated decisions and actionable insights.

Human visual inspection is slow, inconsistent, and prone to fatigue-related errors. An operator can inspect 300-500 parts per hour with an 80% defect detection rate. A computer vision system processes thousands of parts per minute with accuracy above 99%. The difference in product quality, defect cost reduction, and production speed is transformative.

But computer vision goes far beyond industrial inspection. In retail, it analyzes in-store customer behavior to optimize layouts and staffing. In logistics, it reads codes, labels, and documents automatically. In security, it detects anomalies and relevant events in video feeds. In healthcare, it assists in medical image analysis with diagnostic accuracy comparable to specialists.

At Soamee we develop custom computer vision solutions with the most advanced architectures: from YOLO models for real-time detection to deep convolutional networks for high-precision classification. We work with static images, streaming video, and multi-camera feeds, deploying on cloud, edge devices, or on-premise GPUs based on each project's latency and privacy requirements.

99.5%

Detection accuracy

<50ms

Real-time latency

-90%

Undetected defects

24/7

Continuous inspection

Applications

Computer vision for your industry

Each solution is trained and optimized for your specific use case, with custom datasets and performance metrics tailored to your requirements.

Quality control

Automated visual inspection on production lines: defect detection, assembly verification, dimensional measurement, and quality classification. Systems that operate at real production speeds with accuracy above 99%. Drastic reduction in false positives and defective products reaching customers.

OCR & document processing

Text and structured information extraction from any document type: invoices, forms, labels, license plates, checks, medical prescriptions. Advanced OCR combined with layout and semantic understanding to process complex documents with tables, signatures, and stamps.

Video analytics

Real-time analysis of video feeds: people counting, behavior detection, heat maps, dwell time, queue detection, and anomalous events. Ideal for retail (layout optimization), transport (flow management), and security (incident detection).

Object detection & classification

Models trained to detect, locate, and classify specific objects in images or video. From products on shelves to industrial components, vehicles, wildlife, or any object relevant to your use case. Training with your data for maximum accuracy in your domain.

Medical imaging

Diagnostic assistance through automated analysis of medical images: X-rays, CT scans, MRIs, histological and dermoscopic images. Early anomaly detection, anatomical structure segmentation, and finding quantification. Always as a support tool for specialists.

Retail analytics

Computer vision for commerce: planogram analysis (facing verification), out-of-stock detection on shelves, customer flow analysis, product recognition in self-checkout, and display monitoring. Visual data that translates into business decisions.

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Technologien

Computer vision stack

TensorFlow PyTorch OpenCV YOLOv8 Detectron2 Hugging Face Google Cloud Vision AWS Rekognition Azure Computer Vision ONNX TensorRT Python CUDA Docker NVIDIA Triton FastAPI Roboflow Label Studio
Process

From visual need to model in production

A rigorous process that ensures accuracy and performance under real operating conditions.

Definition & dataset

We define the objects/defects to detect, collect representative images from the real environment, and create a labeled dataset with precise annotations. Data augmentation to maximize diversity.

01

Model training

We select the optimal architecture (YOLO, ResNet, EfficientNet, ViT) and train with your dataset. We iterate on hyperparameters until reaching target metrics in precision and recall.

02

Optimization & deployment

We optimize the model for the target hardware (GPU, edge device, cloud). Quantization, pruning, and conversion to efficient formats (TensorRT, ONNX). Deployment with REST API and monitoring.

03

Production validation

Exhaustive testing under real conditions: lighting variations, angles, wear. Continuous fine-tuning with new production data. Alerts on performance degradation.

04
FAQ

Häufig gestellte Fragen about computer vision

How many images do I need to train a model?
It depends on the complexity of the problem. For simple defect detection, 200-500 labeled images are usually sufficient with transfer learning and data augmentation. For complex problems with many classes or variations, 1000-5000+ images may be needed. We start with what you have and apply techniques to maximize performance with limited data.
Can it work in real time on a production line?
Yes. With optimized models (YOLOv8, TensorRT) and appropriate hardware (NVIDIA GPUs or edge devices like Jetson), we achieve latencies of 20-50ms per image, sufficient for high-speed production lines. We evaluate your speed and accuracy requirements to select the optimal architecture and hardware.
Can it be deployed on-premise without internet connection?
Yes. Computer vision models run locally once trained. We can deploy on on-premise GPUs, edge devices (NVIDIA Jetson, Intel NUC), or local servers without internet connection. Ideal for industrial environments with strict security or latency requirements.
What accuracy can be expected?
For well-defined tasks with quality data, achieving 95-99.5% accuracy is common. Accuracy depends on: dataset quality, problem complexity, lighting conditions, and object variability. We define clear metrics (precision, recall, mAP) and validate them under real conditions before considering the system production-ready.
How much does a computer vision project cost?
Cost varies by complexity, but a typical project has three components: model development (labeling, training, optimization), infrastructure (GPUs, cameras, edge devices), and maintenance (periodic retraining, monitoring). In our free consultation we evaluate your case and provide a detailed estimate with expected ROI.
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Give intelligent vision to your business

We help you identify where computer vision can generate the greatest impact on your operations and develop a custom solution that works under real conditions.

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