Computer Vision for Real-World Conditions

Deploy computer vision with accurate object detection, segmentation, and tracking in any lighting, weather, or challenging conditions.

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Trusted and top rated tech team

Vision models fail in real-world conditions

Lab-trained models achieve high accuracy on clean datasets but fail when lighting shifts, objects are partially occluded, or camera angles change. Production accuracy drops from 95% to 60% in challenging conditions, edge deployment introduces latency issues, and annotation costs block rapid iteration. We build computer vision systems with data augmentation, multi-sensor fusion, and edge optimization so models maintain accuracy in variable lighting, weather, and real-world environments.

Our capabilities include:

Object detection and classification systems
Image segmentation and semantic analysis
Facial recognition and biometric verification
OCR and document processing automation
Real-time video tracking and activity recognition
Edge deployment for low-latency inference

Who we support

Computer vision shouldn’t require perfect conditions. We help engineering teams deploy vision systems with sensor fusion, robust preprocessing, and edge optimization so models perform reliably despite lighting variations and environmental challenges.

Teams Deploying Vision at Scale

Your models work in controlled environments but fail when deployed across thousands of cameras with varying lighting, angles, and weather conditions. Accuracy degrades in production, edge devices lack processing power, and retraining costs escalate as you discover real-world edge cases deployment testing missed.

Systems Requiring Real-Time Processing

Your autonomous vehicles, robotics, or surveillance systems need sub-100ms inference but cloud latency introduces delays. Models are too large for edge hardware, video streaming consumes bandwidth, and detection failures occur during critical decision windows when milliseconds determine safety outcomes.

Companies With Labeling Bottlenecks

Your vision projects stall waiting for thousands of labeled images but manual annotation is expensive and slow. Data collection can't capture rare scenarios, synthetic data generation expertise doesn't exist internally, and model improvement cycles stretch from weeks to months.

Ways to engage

We offer a wide range of engagement models to meet our clients’ needs. From hourly consultation to fully managed solutions, our engagement models are designed to be flexible and customizable.

Staff Augmentation

Get access to on-demand product and engineering team talent that gives your company the flexibility to scale up and down as business needs ebb and flow.

Retainer Services

Retainers are perfect for companies that have a fully built product in maintenance mode. We'll give you peace of mind by keeping your software running, secure, and up to date.

Project Engagement

Project-based contracts that can range from small-scale audit and strategy sessions to more intricate replatforming or build from scratch initiatives.

We'll spec out a custom engagement model for you

Invested in creating success and defining new standards

At Curotec, we do more than deliver cutting-edge solutions — we build lasting partnerships. It’s the trust and collaboration we foster with our clients that make CEOs, CTOs, and CMOs consistently choose Curotec as their go-to partner.

Helping a Series B SaaS company refine and scale their product efficiently

Why choose Curotec for computer vision?

Our engineers build object detection pipelines, implement semantic segmentation models, and deploy edge inference systems. We configure sensor fusion architectures, create synthetic training datasets, and optimize models for real-time processing. You get production vision systems maintaining accuracy across lighting conditions, weather variations, and challenging angles without hiring computer vision specialists.

1 Extraordinary people, exceptional outcomes

Our outstanding team represents our greatest asset. With business acumen, we translate objectives into solutions. Intellectual agility drives efficient software development problem-solving. Superior communication ensures seamless teamwork integration.

2 Deep technical expertise

We don’t claim to be experts in every framework and language. Instead, we focus on the tech ecosystems in which we excel, selecting engagements that align with our competencies for optimal results. Moreover, we offer pre-developed components and scaffolding to save you time and money.

3 Balancing innovation with practicality

We stay ahead of industry trends and innovations, avoiding the hype of every new technology fad. Focusing on innovations with real commercial potential, we guide you through the ever-changing tech landscape, helping you embrace proven technologies and cutting-edge advancements.

4 Flexibility in our approach

We offer a range of flexible working arrangements to meet your specific needs. Whether you prefer our end-to-end project delivery, embedding our experts within your teams, or consulting and retainer options, we have a solution designed to suit you.

Computer vision capabilities for production environments

YOLO Model Implementation

Deploy YOLO or Faster R-CNN models detecting and localizing multiple objects in real-time with bounding boxes so applications process video streams at 30+ FPS.

Thermal Camera Integration

Integrate thermal imaging sensors with RGB cameras detecting objects in complete darkness or dense fog so systems maintain functionality when optical cameras fail.

Synthetic Data Generation Pipeline

Build generative AI pipelines creating labeled training images for rare scenarios like extreme weather or unusual angles so models learn without expensive manual annotation.

TensorRT Optimization for Edge

Optimize models with TensorRT or ONNX Runtime reducing inference latency to under 50ms on edge devices so autonomous systems respond within safety-critical timeframes.

Image Preprocessing for Adverse Conditions

Implement denoising algorithms removing rain, fog, and blur before inference so detection accuracy remains consistent despite weather conditions.

Multi-Camera Calibration System

Configure calibration systems fusing multiple camera viewpoints into 3D scenes so applications track objects across cameras without losing identity.

Infrastructure for computer vision systems

Object Detection Frameworks

We implement object detection models identifying and localizing multiple objects in images with bounding boxes and confidence scores.

YOLO (You Only Look Once) — Real-time object detection framework processing images at 30+ FPS with single-pass architecture for speed
Faster R-CNN — Two-stage detection model achieving high accuracy through region proposal networks and refined bounding box predictions
RetinaNet — Single-stage detector using focal loss to handle class imbalance and detect small objects with high precision
EfficientDet — Scalable detection architecture balancing accuracy and efficiency through compound scaling across backbone and feature networks
Mask R-CNN — Extension of Faster R-CNN adding instance segmentation capabilities to detect object boundaries at pixel level
DETR — Transformer-based detection eliminating hand-designed components like anchor boxes through end-to-end training

Image Segmentation & Analysis

Segmentation models classify every pixel in images identifying object boundaries precisely for medical imaging and autonomous systems.

U-Net — Medical imaging architecture with encoder-decoder structure excelling at biomedical image segmentation with limited training data
DeepLab — Google semantic segmentation model using atrous convolution and spatial pyramid pooling for multi-scale context capture
Segment Anything (SAM) — Meta foundation model generating masks for any object in images through prompt-based segmentation
Mask2Former — Unified architecture handling semantic, instance, and panoptic segmentation with transformer-based mask classification
SegFormer — Efficient transformer model for semantic segmentation combining hierarchical features with lightweight decoder
MMSegmentation — OpenMMLab toolbox providing 50+ segmentation algorithms with standardized training and evaluation pipelines

Deep Learning Frameworks

Curotec builds vision models using deep learning frameworks training CNNs on GPUs with distributed computing support.

PyTorch — Facebook research framework providing dynamic computation graphs, extensive vision libraries, and production deployment tools
TensorFlow — Google platform offering comprehensive ecosystem for training, serving, and deploying computer vision models at scale
Keras — High-level API simplifying neural network development with intuitive interfaces for rapid prototyping and experimentation
MXNet — Apache framework supporting flexible programming models and efficient multi-GPU training for large-scale vision tasks
JAX — Google research library enabling automatic differentiation and XLA compilation for high-performance vision model training
ONNX — Open format for model interchange enabling trained models to move between frameworks and deployment environments

Computer Vision Libraries

Vision libraries provide preprocessing, feature extraction, and traditional CV algorithms complementing deep learning approaches efficiently.

OpenCV — Industry-standard library offering 2,500+ optimized algorithms for image processing, object detection, and camera calibration
scikit-image — Python library providing algorithms for filtering, morphology, segmentation, and geometric transformations on images
Pillow — Python imaging library handling image file I/O, basic transformations, and format conversions across formats
Albumentations — Fast augmentation library applying transformations like rotation, cropping, and color adjustments during training
imgaug — Data augmentation library generating diverse training samples through geometric, color, and noise transformations
SimpleCV — Wrapper library simplifying computer vision development with intuitive APIs for common tasks like blob detection

Edge Deployment & Optimization

We optimize models for edge devices using quantization and pruning achieving real-time inference on constrained hardware.

TensorRT — NVIDIA optimization SDK accelerating inference on GPUs through layer fusion, precision calibration, and kernel auto-tuning
ONNX Runtime — Cross-platform inference engine optimizing models for CPU, GPU, and specialized accelerators with minimal latency
OpenVINO — Intel toolkit optimizing vision models for CPUs, integrated GPUs, and VPUs with model compression techniques
TensorFlow Lite — Mobile and embedded deployment framework reducing model size and enabling on-device inference for smartphones
Core ML — Apple framework optimizing models for iPhone and iPad using Neural Engine for efficient on-device processing
MediaPipe — Google framework providing optimized ML pipelines for real-time perception on mobile and edge devices

MLOps for Computer Vision

We build MLOps platforms for vision model training, versioning, deployment, and monitoring across the production lifecycle.

Roboflow — End-to-end platform managing datasets, annotations, training, and deployment for computer vision projects
Weights & Biases — Experiment tracking system logging training runs, visualizing model performance, and comparing vision architectures
ClearML — MLOps platform automating experiment management, dataset versioning, and model deployment for CV workflows
DVC — Data version control tracking large image datasets and model files with Git-like versioning capabilities
Label Studio — Data labeling tool supporting bounding boxes, polygons, and segmentation masks with quality control workflows
Supervisely — Computer vision platform combining annotation, training, and deployment tools with collaborative team features

FAQs about our computer vision services

What vision tasks do you support?

We build object detection, image segmentation, facial recognition, OCR, and video tracking systems. Our engineers implement YOLO for real-time detection, U-Net for medical segmentation, and custom models for industry-specific requirements. We handle both 2D image analysis and 3D spatial reconstruction.

How do you handle low-light or adverse weather?

We implement sensor fusion combining RGB, thermal, and LiDAR data so systems maintain accuracy when individual sensors fail. Preprocessing pipelines remove rain, fog, and motion blur before inference. Models are trained on augmented datasets simulating challenging conditions.

Can models run on edge devices?

Yes. We optimize models with TensorRT or ONNX Runtime reducing inference time to under 50ms on edge hardware. Quantization and pruning reduce model size by 4-10x while maintaining accuracy. We deploy on NVIDIA Jetson, Intel NUCs, and mobile devices.

How do you reduce data annotation costs?

We use synthetic data generation creating labeled training images for rare scenarios, reducing manual annotation by 60-80%. Active learning identifies which images need labeling, prioritizing edge cases. We implement semi-supervised techniques training on partially labeled datasets.

What accuracy can we expect in production?

Production accuracy depends on environment variability and model complexity. Well-implemented systems maintain 85-95% accuracy across lighting and weather conditions. We establish baseline metrics, monitor drift, and implement retraining pipelines maintaining performance as conditions change.

How long does CV implementation take?

Proof-of-concept with existing models takes 3-4 weeks. Custom model development and training takes 2-3 months depending on dataset size and complexity. Production deployment with edge optimization and monitoring takes an additional 4-6 weeks. We deliver incrementally so teams see progress.

Ready to have a conversation?

We’re here to discuss how we can partner, sharing our knowledge and experience for your product development needs. Get started driving your business forward.