Scenario-specific training
Models are tuned to customer risk sources, camera perspectives, and false-positive patterns.
Models trained for customer scenes
AI vision, field-ready
Customers provide targets, scenes, and samples; we handle data curation, model training, deployment, and quarterly iteration.
Few-shot startPrivate deploymentQuarterly updatesField validation
96.8%
Sample-conditioned accuracy
<3%
Scene-tuned false-alarm target
Quarterly updates
Update cadence
Define target
Label samples
Deploy & iterate
Service characteristics
You define the recognition target, we deliver a deployable vision model
VisionFire is the standardized entry point; the core capability is turning customer-specific targets such as crop maturity, industrial defects, and safety behavior into deployable models.
Flexible deployment
Supports non-intrusive access, edge deployment, private servers, or API integration.
Ongoing model operations
Quarterly reviews prevent the model from drifting away from real operations.
Delivery process
The delivery framework behind custom AI vision
From target definition to labeling, model cold start, field validation, private deployment, and quarterly updates, the framework makes custom delivery repeatable.
Target and scene definition
Define what to recognize, where it runs, who receives the output, and whether alerts, grading, or inspection results are required.
Sample and labeling protocol
Structure positive samples, negative samples, hard cases, and false-positive triggers into trainable data.
Few-shot cold start
Use few-shot object detection to validate limited-sample cases such as apple ripeness and pinecone maturity.
Field validation and thresholds
Validate accuracy, false alarms, and inference latency under real lighting, occlusion, angle, and operating conditions.
Private or edge deployment
Select private servers, edge nodes, or API integration based on data security, network conditions, and latency needs.
Quarterly feedback loop
Feed new samples, false alarms, and business changes back into model management to keep recognition useful.
Core technical parameters
Clear metrics for accuracy, false alarms, latency, and deployment fit
Each metric is tied to training conditions, usage context, or delivery value so customers can compare options and secure internal approval.
AI vision, field-ready
| Metric | Value | Condition | Why it matters |
|---|---|---|---|
| Fire recognition accuracy | 96.8% | Based on current training and validation records | Communicates capability under current sample conditions, not an unconditional guarantee for every site. |
| False alarm rate | <3% | Tuned separately for chemical plants, charging sites, and small premises | Presented as a scene-tuning target that still requires review against real camera angles, lighting, and interference. |
| Alert response time | Real-time / near-real-time | Real-time or near-real-time modes | Response mode depends on site network, edge nodes, and deployment form. |
| Lead time | Project-record based | Requires confirmation through real-scenario review | Prevents demo or sample conclusions from becoming formal site promises. |
| Model update cadence | Quarterly updates | Updated based on business and model changes | Moves from one-off delivery to ongoing capability growth. |
| Deployment modes | Non-intrusive / edge / private / API | Supports offline training and local deployment | Balances security, IT fit, and rollout speed. |
| Custom use cases | Apple ripeness / Pinecone maturity / Industrial defects / Safety behavior | Self-supervised Learning & Few-shot Object Detection | Enables rapid cold-start with minimal samples, reducing training cycles and data costs. |
Metric
Fire recognition accuracy
- Value
- 96.8%
- Condition
- Based on current training and validation records
- Why it matters
- Communicates capability under current sample conditions, not an unconditional guarantee for every site.
Metric
False alarm rate
- Value
- <3%
- Condition
- Tuned separately for chemical plants, charging sites, and small premises
- Why it matters
- Presented as a scene-tuning target that still requires review against real camera angles, lighting, and interference.
Metric
Alert response time
- Value
- Real-time / near-real-time
- Condition
- Real-time or near-real-time modes
- Why it matters
- Response mode depends on site network, edge nodes, and deployment form.
Metric
Lead time
- Value
- Project-record based
- Condition
- Requires confirmation through real-scenario review
- Why it matters
- Prevents demo or sample conclusions from becoming formal site promises.
Metric
Model update cadence
- Value
- Quarterly updates
- Condition
- Updated based on business and model changes
- Why it matters
- Moves from one-off delivery to ongoing capability growth.
Metric
Deployment modes
- Value
- Non-intrusive / edge / private / API
- Condition
- Supports offline training and local deployment
- Why it matters
- Balances security, IT fit, and rollout speed.
Metric
Custom use cases
- Value
- Apple ripeness / Pinecone maturity / Industrial defects / Safety behavior
- Condition
- Self-supervised Learning & Few-shot Object Detection
- Why it matters
- Enables rapid cold-start with minimal samples, reducing training cycles and data costs.
Standardized product entry
“Huiyan VisionFire” standardized smart safety system
Targeting high-risk zones like chemicals storage, EV charging docks, and micro-business areas, providing ready-to-use safety monitoring.
Scenario
Hazchem and lithium sites
Specialized negative-sample training addresses steam, welding glare, and complex heat sources to reduce false alarms in high-risk plants.
Scene tuned
False-alarm control
Human review
Response boundary
Quarterly
Model review
Scenario
Charging and storage sites
Focuses on early thermal runaway warnings and headlight suppression for more reliable station-level monitoring.
Project review
Warning window
Real-time
Monitoring mode
API
Integration mode
Scenario
Small-premises scenario
For workshops, timber sites, and small shops where budgets are tight but risk remains real, with an emphasis on affordable deployment.
Standard template
Scenario package
Light deployment
Deployment mode
Policy friendly
Policy fit
Custom service examples
Apple ripeness, pinecone maturity, and industrial defects can all enter custom training
Beyond standard safety monitoring, we support custom target recognition such as crop maturity (e.g. apple ripeness, pinecone harvest window) and industrial quality checks.
Industrial inspection
Defect inspection, surface consistency check, and precision tolerance identification, supporting seamless line and MES integration.
Safety behavior recognition
Utilizes pose estimation to verify PPE compliance (helmet, harness, goggles), geofence entry, falls, and unauthorized operations.
Agriculture & custom target detection
Tailored recognition for agriculture (e.g. apple ripeness sorting, pinecone harvest window) as well as smart warehousing.
Submit custom request
Submit a custom vision request and validate feasibility first
Share your industry, recognition target, and site conditions. We will respond within one business day with an initial feasibility view.