Core Technology

Two Technology Pillars · Down to Engineering Reality

One axis is 'an industrial algorithm production line — turning high-cost R&D into replicable output.' The other is 'cloud-edge perception and closed-loop integration that runs reliably on-site and plugs into business processes.' Edge acceleration, system integration, and open APIs all serve these two core axes.

≥97%

Typical Scene Accuracy

40ms

Single-Stream Latency

70%

Model Size Reduction

4–8 wks

Avg. Time-to-Production

Core Pillars

Two Core Technology Pillars

The two axes address the algorithm production side and the field deployment side, together forming a complete technology closed loop for GSmart industrial vision AI.

Pillar 1 · Models & Data

Full-Pipeline Industrial Vision Model Production

GTraining Platform

Covers the complete production pipeline from raw video/images to deployable models, solving the core challenges of high cost, long cycles, and poor replicability in industrial algorithm development.

Data Engineering

Multimodal LLM-assisted annotation, 5–8× efficiency gain
Auto data cleaning, deduplication & quality scoring
Scene augmentation: simulating low-light, dust, occlusion

Model Training

Supports 20+ detection / segmentation / tracking architectures
Auto hyperparameter tuning, distributed training acceleration
Knowledge distillation: 70% size reduction, 50% inference speedup

Validation & Release

3-axis evaluation: accuracy / speed / missed-detection rate
A/B testing & canary version management
Export to ONNX / TensorRT / NCNN and more

Edge Deployment

One-click push to edge devices with OTA hot updates
Version rollback & deployment status monitoring
Average time-to-production: 4–8 weeks

5–8×

Annotation Efficiency

20+

Model Architectures

70%

Size Reduction (Distillation)

4–8w

Avg. Time-to-Production

View Technical Details

Pillar 2 · Perception & Closed Loop

Cloud-Edge Collaborative On-Site Perception & Business Integration

GSmart Platform + Edge Device

Converts visual recognition results into actions that enter business workflows, maintaining stable operation under extreme conditions — low connectivity, dust, strong light — and integrating seamlessly with existing business systems.

Visual Perception

Multi-frame fusion enhancement for low-texture / occlusion / glare / dust
Multi-stream concurrent scheduling, single-stream inference latency ≤ 40ms
Cross-camera target tracking and behavior recognition

Inference Acceleration

Edge-side quantization acceleration for Jetson / Ascend / Rockchip
Local inference under poor/no connectivity with auto-sync on reconnect
Centralized cloud management for model versions & configuration push

Alert Integration

Standard protocol integration with PLC / DCS / MES
Linkage with ticketing systems, dispatch platforms & dashboards
Alert grading, suppression rules & resolution status tracking

Continuous Improvement

False-positive feedback auto-loops back to training to reduce alert noise
Scene data accumulation supports ongoing model iteration
Typical scene recognition accuracy ≥ 97%

≥97%

Typical Scene Accuracy

40ms

Single-Stream Latency

99.9%

System Uptime

Offline

Low/No-Connectivity Ready

View Technical Details

Underlying Technology

The core algorithms and engineering capabilities underpinning both pillars — determining accuracy ceilings and deployment efficiency in industrial environments.

Vertical Industrial Foundation Models

Purpose-built lightweight models pre-trained on massive industrial-scene data, covering people, vehicles, equipment, gauges, fire & smoke. Accuracy ≥ 90%, ready to use as an industry foundation for rapid fine-tuning.

Knowledge Distillation & Model Compression

Transfers large-model capabilities to lightweight networks: 70% size reduction, 50% inference speedup, ≤2% accuracy loss. Edge devices run efficiently with significantly lower compute and energy costs.

AI Self-Training Platform

Fully automated pipeline from data cleaning → annotation → tuning → evaluation with continuous and incremental learning. Customers can maintain their own algorithm systems independently, ending reliance on external annotation teams.

Research

Research Directions

GSmart collaborates with universities and research institutes on continuous innovation in industrial vision algorithms, edge computing architecture, and safety AI.

Smart Transportation

Driver Vital-Sign Monitoring Algorithm

Non-contact fusion detection of heart rate / HRV / respiration, providing 3–10 minute early warning for cardiovascular health events.

Manufacturing

Equipment Predictive Maintenance Model

Multimodal anomaly detection fusing vibration, temperature, and visual signals to identify equipment failures in advance.

Smart Mining

Open-Pit Mine Drone Inspection & Edge AI

Joint drone + edge inference deployment for real-time safety inspection across large open-pit mine areas.

Smart Port

Port Conveyor Jam & Spillage Detection

Low-light and rain/fog enhancement to improve conveyor belt detection robustness at night and in adverse weather.

Supporting Engineering Capabilities

Built in parallel with both pillars — each can be explored independently based on project stage and role.

Edge Inference & Quantization

Quantization pruning and multi-stream concurrency on Jetson / Ascend platforms, offline operation, OTA management.

Learn More

Scene Closed Loop & System Integration

Integration with PLC, alerting platforms, and ticketing systems with linked response timing and full audit trails.

Learn More

Open API · SDK · Private Deployment

Standard interfaces, private deployment & SaaS options, developer portal & sandbox — supporting ISV and integrator delivery.

Learn More

Schedule a Technical Discussion

The GSmart team can provide solution assessments, algorithm documentation, test reports, and technical demos tailored to your site conditions.