AI algorithm

Custom AI Algorithm Training

Custom AI model training workflow for uploading field materials, GPU training, model evaluation, and edge deployment to AI cameras, AI edge boxes, and AI NVRs.

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XINHUOAI extends the standard algorithm library when a project needs a new, deployable model for a real industrial scene. The workflow begins with an operational definition: what must be recognized, where it appears, what should trigger an event, and which similar objects or normal actions must be excluded.

Training Loop

Create the task and define the target, scene, output device, and success criteria.
Upload images or video frames from the real site.
Clean, annotate, split, and review the dataset.
Train with GPU resources and evaluate precision, recall, false positives, and missed detections.
Export and deploy the model to AI cameras, AI edge boxes, or AI NVRs.
Feed back false positives and missed examples for continuous iteration.

Offline Runtime

XINHUOAI is used to train and export models. After deployment, real-time recognition, alarms, snapshots, and evidence records run on local AI devices. The model does not require external internet for daily inference.

Algorithm deployment notes

From a site request to a deployable custom detection model

A custom algorithm is an engineering task built around a concrete visible target or action. It becomes deliverable when the project defines the image evidence, samples the real camera environment, labels representative examples, tests a version outside the training data, and documents how the result becomes a usable event.

Start with the decision, not the model name

Describe the target object, required behaviour, normal exceptions, camera locations, event recipient, and the consequence of a missed or incorrect event. This gives the data and engineering teams a stable definition of what belongs in the training and validation material.

Gather footage from the intended deployment

Training material should include target examples and difficult normal scenes across lighting, shift, weather, equipment, uniform, background, distance, and occlusion differences. Images from a different camera type or a clean demonstration environment may not represent the deployment view.

Record model and configuration changes

Maintain a version record for the model, class labels, confidence policy, camera region, schedule, duration, and event interval. This makes it possible to compare results after a retraining cycle or a site-rule adjustment without losing the history of the deployment.

Validate with the people who use the result

Review held-out footage and live trial events with the site team. Include missed detections, ambiguous scenes, false alarms, and image-quality limits. The acceptance record should describe the camera conditions and the operational response, not just a headline score.

Related planning: Custom AI model training platform. A new model should remain linked to the real camera task it was created to support.

Project inquiry

Tell us your camera environment and required AI analytics.

For the first version this site uses a lightweight email-based inquiry flow. It avoids a database and keeps the English site simple until inquiry volume justifies a dedicated CRM integration.