Computer Vision

What we deliver

Twara Technologies builds computer vision systems that turn cameras into dependable measuring instruments. Typical applications include spotting defects on a production line, counting items or vehicles, checking that safety equipment is worn, reading labels and meters, and monitoring stock on shelves. We handle the whole chain: camera and lighting choices, data collection and labelling, model development, deployment at the edge or in the cloud, and the review tools and monitoring that keep the system reliable.

This service suits manufacturers, warehouses, retailers, agriculture and infrastructure operators, and any organisation where people currently spend time looking at things to check, count or record them. A short feasibility exercise on your own images is usually the clearest way to find out what is realistic before investing in hardware.

Typical scope

• Visual inspection for defects, missing parts, alignment or surface quality.
• Object detection and counting for people flow, vehicles, livestock, parcels or inventory.
• Safety monitoring such as restricted-zone entry or protective equipment checks.
• Optical character recognition for labels, number plates, meters and serial numbers.
• Image classification and similarity search for product catalogues and quality grading.
• Edge deployment on gateways or industrial PCs, with results sent to dashboards and business systems.

Technologies we work with

• Frameworks: PyTorch for model development, with OpenCV for image processing and classical techniques that are often enough on their own.
• Model families: established detection and segmentation architectures, including widely used YOLO-family detectors, and vision-language models for flexible or low-data tasks.
• Labelling tools: open-source tools such as CVAT or Label Studio, or managed labelling services when volume is high.
• Edge runtimes: ONNX Runtime, NVIDIA TensorRT on Jetson-class devices, or OpenVINO on Intel hardware, depending on the chosen device.
• Cloud services: managed vision APIs from the major clouds for common tasks such as OCR, and cloud GPUs for training and batch inference.
• Video pipelines: RTSP camera streams, GStreamer-based processing and message queues to carry results onwards.

How we choose: start with the simplest approach that meets the acceptance criteria. Classical image processing or an off-the-shelf API can be cheaper and easier to maintain than a custom model; we move to custom training when the task or conditions require it.

How we approach it

1. Define success. Agree what counts as a correct result, what errors cost and what speed is required.
2. Check the scene. Collect sample images or footage and assess camera position, lighting and variability.
3. Prototype. Test pre-trained models and simple methods on your samples to establish a baseline.
4. Collect and label. Build a dataset that covers normal and edge cases, with documented labelling rules.
5. Train and evaluate. Fine-tune, measure on held-out data from your environment and analyse failure cases.
6. Deploy and monitor. Install at the edge or in the cloud, route uncertain cases to people, and watch for drift when conditions change.

Security, privacy and quality

• People in the frame. Images of identifiable people are personal data. The principles described alongside India’s DPDP Rules, 2025, including purpose limitation, data minimisation and storage limitation, shape our designs: process on the device where possible, blur or mask faces when identity is not needed, and keep footage only as long as the purpose requires.
• Biometric restrictions abroad. The EU AI Act bans certain practices, including real-time remote biometric identification for law enforcement in public spaces, and classes some other uses as high risk with strict obligations. We flag such issues early for products used in Europe.
• Risk management. We apply the NIST AI Risk Management Framework to document intended use, known limitations and monitoring plans.
• Device security. Cameras and edge devices are configured with unique credentials, encrypted streams and timely updates, guided by ETSI EN 303 645.
• Quality. Evaluation on held-out data from the real site, tests across lighting and seasonal conditions, versioned datasets and models, and regular review of misclassified cases.

Engagement options

• Feasibility study: test whether the task is achievable with your cameras and sample data.
• Pilot deployment: a working system on one line, site or camera group, measured against agreed criteria.
• Production rollout: hardening, scaling across sites, monitoring and integration.
• Model care: periodic retraining, drift monitoring and support as conditions change.

Contact us to discuss what you need your cameras to see.