Our Computer Vision solutions leverage cutting-edge technologies like YOLO for fast object detection and EfficientNet for accurate image classification. We deliver applications in real-time video analytics, facial recognition, and augmented reality, turning visual data into actionable insights. Spanning industries like retail, security, healthcare, and manufacturing, our solutions drive efficiency and value.
Core Capabilities
Our Computer Vision services offer a comprehensive set of solutions to address the diverse needs of visual data processing, tailored to deliver accuracy, speed, and scalability.
Object Detection
- Object detection identifies and localizes objects within images or video streams, allowing for real-time tracking and analysis.
- Leveraging the YOLOv7 architecture, we develop high-performance models that detect and track objects with speed and precision, making our solutions ideal for applications like surveillance, autonomous driving, and retail analytics.
Image Classification
- Image classification categorizes visual data into predefined classes, supporting applications like defect detection, medical diagnostics, and content moderation.
- Utilizing deep learning models like EfficientNet and ResNet, we create image classification models with high accuracy, capable of identifying intricate visual patterns for tasks in sectors such as healthcare, manufacturing, and e-commerce.
Synthetic Data Generation
- Synthetic data generation uses models to create realistic images and videos, providing valuable training data for scenarios with limited or sensitive datasets.
- With Generative Adversarial Networks (GANs) like StyleGAN and CycleGAN, we produce high-quality synthetic data that enhances model training by augmenting datasets, allowing for robust and comprehensive model performance even with limited real-world data.
Edge Processing and Model Optimization
- Edge processing brings computation to the device, reducing latency by processing data locally rather than relying on centralized servers.
- We optimize models using Quantization, Pruning, and Distillation, allowing for low-latency, high-speed inference on edge devices, crucial for applications in mobile AR, IoT, and wearable tech.
Advanced Computer Vision Techniques and Technologies
1. Multi-Object Tracking (MOT)
MOT enables tracking of multiple objects across video frames, essential for applications where real-time, continuous object tracking is needed.
Enables complex applications like sports analytics, traffic monitoring, and autonomous navigation by maintaining object identities over time.
2. 3D Object Detection and Depth Estimation
Using 3D object detection, we extract depth information and recognize objects in three-dimensional space, crucial for AR, VR, and robotics.
Enhances spatial awareness and interaction within environments, ideal for applications in autonomous systems and immersive technologies.
3. Pose Estimation
Pose estimation identifies the orientation and position of objects, particularly human bodies, in images or videos, useful in human activity recognition.
Supports applications in sports analysis, healthcare monitoring, and interactive gaming, enabling more accurate and engaging user experiences.
4. Semantic and Instance Segmentation
Semantic segmentation labels each pixel in an image to identify object boundaries, while instance segmentation distinguishes between multiple instances of the same object.
Provides precise object recognition and localization, ideal for medical imaging, autonomous driving, and industrial inspection.
Technology Stack
Our Computer Vision solutions are powered by industry-leading tools and platforms to ensure top-tier performance and scalability
Computer Vision Frameworks
OpenCV, Detectron2, PyTorch, TensorFlow, MMDetection for object detection, FastAI for simplified deep learning applications, PaddleOCR.
Pre-trained Model Libraries
Torchvision, Keras Applications for ResNet, EfficientNet, and YOLO, Hugging Face Transformers for vision-text models, Vision Transformer (ViT), ConvNeXt, MobileNet and ShuffleNet.
Cloud Vision APIs
AWS Rekognition, Google Vision API, Microsoft Azure Computer Vision, IBM Watson Visual Recognition, DeepAI API, NVIDIA AI Enterprise for optimized cloud vision processing.
Deployment and Optimization
NVIDIA GPUs, TensorRT, MLflow, Kubernetes for model orchestration, Triton Inference Server, Edge TPU and Coral for ultra-low-latency, ONNX Runtime for cross-platform model deployment, Ray Serve, Quantization.
Key Use Cases
Facial Recognition for Security and Personalization
Using CNN-based facial recognition models, we provide secure, accurate identity verification, access control, and personalized customer experiences.
Access control in corporate environments, personalized marketing in retail, and identity verification in banking.
Real-Time Video Analytics for Surveillance and Monitoring
Our real-time video analytics solutions analyze live video feeds, detecting activities, counting objects, and identifying anomalies, providing insights for improved decision-making.
Security monitoring, traffic analysis, and activity recognition in public spaces.
Augmented Reality (AR) for Enhanced User Experiences
Powering AR applications with object and scene recognition, we enable interactive user experiences for gaming, retail, and virtual try-ons.
Virtual try-on features in e-commerce, interactive gaming experiences, and AR-based training simulations.
Defect Detection in Manufacturing
Our image classification models detect product defects with high accuracy, automating quality control in manufacturing and reducing error rates.
Identifying defects in electronics assembly, flaw detection in automotive manufacturing, and anomaly detection in food processing.