Slow or inconsistent visual inspection
Manual inspection varies between people and shifts and misses subtle defects.
04 / Hybrid Intelligence
Computer Vision Solutions
Cameras are inexpensive and everywhere. Turning what they capture into reliable measurements, detections and alerts is harder. Lighting changes, angles vary, defects are rare and conditions in the field are nothing like the lab. We build computer vision systems designed for real environments.
At a glance
- Service
- Computer Vision Solutions
- Pillar
- Hybrid Intelligence
- Typical starting point
- Visual data and site assessment
- Typical first phase
- 8 to 14 weeks for a pilot including data collection
- Key technologies
- Computer Vision, Edge AI, Multimodal AI
Durations depend on scope, data readiness and integration needs.
What is Computer Vision?
Short answer
Computer vision is a field of artificial intelligence that enables computers to interpret images and video, performing tasks such as classifying images, detecting and tracking objects, segmenting regions, reading text, measuring dimensions and recognising activities, often in real time and on edge devices.
Computer vision projects succeed or fail on data and conditions more than on algorithms. A model trained on a few hundred clean images may perform well in testing, then struggle with glare, dust, new product variants or a camera that has moved slightly.
We start with the environment: where cameras will be, what they will see, how lighting and conditions vary and what the system must detect. Data is collected in those conditions, labelled carefully and used to train models that are validated on data from different days, shifts and sites. Deployment choices, cloud or edge, follow from latency, bandwidth and privacy needs. People review uncertain detections, and their decisions improve the model.
Business problems
What problems does Computer Vision solve?
These are the situations where organisations most often find this service worthwhile.
Monitoring too many cameras
Security or operations teams cannot watch every feed.
Manual counting and measurement
Staff count items, measure dimensions or check presence by eye.
Safety compliance gaps
Unsafe behaviour or missing protective equipment goes unnoticed.
Unreadable information in images
Meters, labels, plates or documents in images need reading at scale.
Remote sites without visibility
Assets in the field cannot be checked regularly.
Use cases
Where can Computer Vision be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Manufacturing quality | Surface defect detection, assembly verification and dimensional checks. |
| Retail | Shelf availability, planogram compliance and footfall analytics without identifying individuals. |
| Agriculture | Crop disease detection, fruit grading and livestock monitoring. |
| Infrastructure | Crack, corrosion and damage detection from drone or fixed camera imagery. |
| Logistics | Package dimensioning, damage detection and label reading. |
| Workplace safety | PPE compliance and restricted zone monitoring as support for safety teams. |
Who should consider it
- Manufacturers seeking consistent quality inspection
- Operators of warehouses, retail stores or sites with cameras
- Infrastructure and utilities organisations
- Agritech and food processing companies
How AARHIT approaches it
How we deliver Computer Vision
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Use case and site assessment
We define what must be detected, acceptable error rates and conditions, and assess camera positions, lighting and connectivity.
Output: requirements and site assessment.
Stage 2 of 6
Data collection plan
Image or video capture is planned to cover variation in conditions, products and rare cases.
Output: data collection protocol.
Stage 3 of 6
Labelling
Data is labelled to clear guidelines with quality checks and domain expert review.
Output: labelled dataset with quality metrics.
Stage 4 of 6
Model development
Detection, classification or segmentation models are trained and validated on data from separate days and locations.
Output: validated models with performance by condition.
Stage 5 of 6
Deployment
Models are deployed to cloud or edge hardware with integration into alerts, dashboards or control systems.
Output: deployed pilot system.
Stage 6 of 6
Monitoring and improvement
Performance is monitored, uncertain cases are reviewed and models are retrained with new data.
Output: monitoring and retraining process.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Models | YOLO family detectors, Vision Transformers, segmentation models, vision language models |
| Frameworks | PyTorch, Ultralytics, OpenCV, Detectron2, MMDetection |
| Labelling | CVAT, Label Studio, active learning workflows |
| Edge deployment | NVIDIA Jetson, Intel OpenVINO, TensorRT, smart cameras |
| Video | RTSP stream processing, NVIDIA DeepStream, GStreamer |
| Cloud | Managed vision services where suitable, GPU inference endpoints |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- More consistent inspection and detection
- Staff attention focused on flagged cases
- Automated counting, measuring and reading at scale
- Faster response to safety or operational events
- Visual records supporting quality and compliance
How long does implementation generally take?
Typical timeline
A computer vision pilot, including data collection, labelling, model development and on-site validation, typically takes 8 to 14 weeks. Rollout to additional lines or sites is faster once data pipelines and deployment patterns exist.
What determines complexity?
- Rarity and subtlety of what must be detected
- Variation in lighting, angles and environment
- Real-time processing requirements
- Number of cameras and sites
- Privacy and regulatory constraints
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Control the imaging where possible
Consistent lighting and camera positions often improve results more than model changes.
Rare defects need special strategies
When defects are rare, anomaly detection, synthetic data or targeted collection may be needed.
Privacy by design
Avoid identifying individuals unless strictly necessary and lawful; blur or discard unnecessary personal data.
Cameras need maintenance
Dirty lenses and moved cameras degrade performance. Include checks in operating routines.
| Risk to consider | How we manage it |
|---|---|
| Performance drop in new conditions | Diverse training data, validation across conditions and drift monitoring. |
| False alarms reducing trust | Tuned thresholds, human review of alerts and feedback loops. |
| Privacy and surveillance concerns | Purpose limitation, anonymisation, access controls and legal review. |
| Label quality issues | Clear guidelines, inter-annotator checks and expert review. |
FAQ
Questions about Computer Vision
How many images are needed to train a computer vision model?
It depends on task complexity and variation. Using pre-trained models, some tasks can start with a few hundred labelled images per class, while subtle defects or highly variable conditions may need thousands. A data assessment gives a realistic estimate.
Can computer vision run without an internet connection?
Yes. Models can run on edge devices such as smart cameras or industrial computers, processing video locally and sending only results when connectivity is available.
What accuracy can computer vision achieve?
Accuracy depends on the task, imaging conditions and data. We agree target metrics such as detection rate and false alarm rate for your use case and measure them on data from real conditions.
Does computer vision work with existing CCTV cameras?
Often, for tasks like presence detection or counting. Precise inspection or reading tasks may need better resolution, positioning or lighting. We assess existing cameras during the site assessment.
How do you handle privacy in video analytics?
By limiting collection to what the purpose requires, avoiding facial recognition unless lawful and justified, anonymising where possible, restricting access and setting retention limits.
Next step
Considering Computer Vision for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.