Computer Vision Development Services
Production-ready computer vision solutions: object detection, image classification, facial recognition, video analytics, and OCR with 95%+ accuracy.
Key Capabilities
Accuracy
State-of-the-art models achieving 95-99% accuracy on production datasets.
Real-time Processing
Process images and video frames in real-time with <50ms latency.
Edge Deployment
Deploy on edge devices (Jetson, Coral, cameras) for low-latency inference.
Computer Vision Solutions
Object Detection & Recognition
Detect and classify multiple objects in images and video with bounding boxes, labels, and confidence scores.
- ✓ YOLOv8, EfficientDet, Faster R-CNN models
- ✓ Custom object detection for your products/assets
- ✓ Real-time processing (30-60 FPS)
- ✓ Small object detection optimization
- ✓ Multi-class detection (100+ classes)
Image Classification & Segmentation
Classify images into categories and perform pixel-level segmentation for precise understanding.
- ✓ Image classification (ResNet, EfficientNet, ViT)
- ✓ Semantic segmentation (U-Net, DeepLabv3)
- ✓ Instance segmentation (Mask R-CNN)
- ✓ Medical imaging analysis
- ✓ Quality inspection and defect detection
Facial Recognition & Analysis
Detect faces, recognize individuals, and analyze facial attributes with privacy-compliant solutions.
- ✓ Face detection and landmark recognition
- ✓ Face verification and identification
- ✓ Age, gender, emotion estimation
- ✓ Liveness detection (anti-spoofing)
- ✓ Privacy-preserving face blurring
OCR & Document Analysis
Extract text and data from documents, forms, invoices, and images with high accuracy.
- ✓ Text detection and recognition (Tesseract, EasyOCR, PaddleOCR)
- ✓ Handwriting recognition
- ✓ Document layout analysis
- ✓ Table and form extraction
- ✓ Multi-language support (100+ languages)
Video Analytics
Analyze video streams for activity recognition, tracking, and anomaly detection.
- ✓ Multi-object tracking (DeepSORT, ByteTrack)
- ✓ Action recognition and activity analysis
- ✓ Crowd counting and density estimation
- ✓ Anomaly detection in surveillance
- ✓ Real-time video processing pipelines
Industry Applications
Retail & E-commerce
- • Visual search and product recommendations
- • Shelf monitoring and planogram compliance
- • Checkout-free stores (Amazon Go-style)
- • Loss prevention and theft detection
Manufacturing & Quality Control
- • Automated visual inspection
- • Defect detection (scratches, dents, etc.)
- • Assembly verification
- • Dimensional measurement
Healthcare & Medical
- • Medical imaging analysis (X-ray, CT, MRI)
- • Cancer detection in pathology slides
- • Diabetic retinopathy screening
- • Skin lesion classification
Security & Surveillance
- • Person and vehicle detection
- • Intrusion detection and perimeter security
- • License plate recognition (ANPR)
- • Suspicious behavior detection
Pricing & Timeline
Investment
- • Object Detection: $15-36K
- • Image Classification: $10-24K
- • OCR System: $12-30K
- • Video Analytics: $24-60K
- • Custom Solution: $36K-240K
Timeline
- • POC: 2-4 weeks
- • MVP: 6-8 weeks
- • Production: 10-16 weeks
- • Data labeling: 2-6 weeks
- • Model training: 1-3 weeks
Case Study
Manufacturing Quality Control
Challenge: Manual visual inspection of 10K parts/day, 8% defect miss rate
Solution: Custom YOLOv8 model for defect detection with edge deployment
Results:
- • Detection accuracy: 98.7%
- • Defect miss rate: 8% → 0.3% (-96%)
- • Inspection speed: 100x faster
- • False positive rate: <1%
Business Impact:
- • Investment: $42K
- • Annual savings: $217K
- • Quality improvement: 85%
- • ROI: 514% first year
Ready to Build Computer Vision Solutions?
Get a free consultation and technical feasibility assessment for your computer vision project.
Start Your CV Project →Frequently Asked Questions
What computer vision tasks can TensorBlue build for us?
Object detection, instance and semantic segmentation, OCR / document understanding, defect detection, multi-camera tracking, pose estimation, and vision-language models (CLIP, BLIP-2, GPT-4V) for retrieval and product search.
Will the vision model run on-device or in the cloud?
Both. We ship cloud inference for high-throughput batch workloads and edge inference (Jetson, Coral, iOS Core ML, Android NNAPI) when latency, privacy, or offline operation matter.
How much labeled data do we need?
With modern transfer learning and synthetic augmentation, focused detectors can reach production-grade accuracy with 1–5K labeled examples per class. We also handle the labeling pipeline (active learning, human-in-the-loop) when needed.