Computer Vision Development Services
Computer vision development for object detection, image classification, video analytics and OCR. Start with representative images, measurable acceptance criteria and a deployment plan.
Key Capabilities
Detection Quality
Measure precision, recall and missed detections on held-out images from your operating environment.
Real-time Processing
Test latency and throughput on the target hardware, including capture, preprocessing, inference and downstream actions.
Edge Deployment
Choose edge hardware against supported operators, memory, power and measured capture-to-result latency.
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
- ✓ Frame-rate and latency evaluation on target hardware
- ✓ Small object detection optimization
- ✓ Classes and annotation rules defined for your dataset
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 explicit access, retention and consent requirements.
- ✓ 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
- ✓ Language coverage evaluated against representative documents
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
Indicative planning ranges, subject to a scoped proposal. Data collection, annotation, camera hardware, integrations and deployment requirements affect cost and schedule. Confirm included deliverables and ongoing operating costs before committing.
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
What to agree with computer vision developers
An annotation contract
Define classes, bounding-box or mask rules, and how to label partially visible objects, ambiguous defects and empty frames. Include approved examples and a process for resolving annotator disagreement. Retain image provenance and dataset versions so a corrected label can be traced through training and evaluation. Split related video frames, production batches or camera sessions together; nearly identical images in both training and evaluation can hide generalization errors.
An acceptance report tied to the workflow
Agree the operating threshold and report missed detections and false alarms by class and condition. Include darkness, glare, occlusion, motion blur and camera movement where relevant. For OCR, separate text recognition errors from incorrect extraction of a business field. For video analytics, count duplicated or missed events as well as frame-level detections. Measure the full time from capture to a usable result, including decoding, queues, preprocessing and downstream calls.
A deployment and recovery contract
Specify input resolution, frame rate, supported devices, output coordinates, class identifiers and API error behavior. Test the exported model on the actual runtime and hardware. Compare output quality after quantization or another optimization; a smaller model does not establish an acceptable error rate or faster end-to-end processing. The ONNX Runtime quantization guide explains calibration and accuracy debugging.
Define behavior for a disconnected camera, dropped frames, resource exhaustion and an unavailable downstream system. Handover should include the model and dataset versions, evaluation report, deployment configuration, update procedure and rollback instructions. Assign an owner for drift review and decide which camera or product changes require a new evaluation.
Example Manufacturing Pilot
Plan a Visual Inspection Workflow
This illustrative workflow is a planning example. It is not a measured customer result. Begin with one inspection station and a defined set of defects rather than automating every production line at once.
Data and Evaluation
- • Collect images across shifts, lighting conditions and part variants.
- • Have inspectors label defects and resolve ambiguous examples.
- • Hold out later production batches to test generalization.
- • Report missed defects and false alarms for each defect category.
Deployment and Acceptance
- • Measure the full capture-to-decision latency at line speed.
- • Run in observation mode before enabling downstream actions.
- • Route uncertain results to an inspector and keep a manual fallback.
- • Agree acceptance thresholds, operating costs and rollback criteria before launch.
Review false rejects, reinspection time and escaped defects alongside model metrics. A useful pilot must improve the inspection process under real operating conditions. Preserve the evaluation dataset, model version and configuration so results can be reproduced after an update.
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.