C++ Core Inference Engine: Self-developed lightweight C++ algorithm kernel, encapsulating underlying hardware operations and algorithm scheduling through standardized interfaces, achieving millisecond-level real-time response capability. Supports dynamic loading of pluggable functional modules; users can expand inspection processes, algorithm models, or hardware adaptation modules via "hot-swapping."

Intelligent Modules: Through standardized SDK interfaces, the platform includes basic algorithm modules for industrial scenarios such as image classification, object detection, and high-precision segmentation.

Hardware Heterogeneous Compatibility: From Intel CPU to NVIDIA GPU, the platform achieves decoupling of algorithms and hardware through a unified computing abstraction layer. Users can freely combine computing units based on cost and performance requirements, protecting existing hardware investments.

Multi-Inference Framework Compatibility: Built-in ONNX Runtime integration, compatible with mainstream inference frameworks like TensorRT and NCNN.

Just install the graphics card driver.

No professional knowledge required; can be used according to SOP after simple training.

Incremental training, rapid iteration.

Inspection processes, algorithm models, or hardware adaptation modules can be expanded via "hot-swapping."

In most scenarios, an RTX 1050 GPU can meet online inspection requirements; for occasions with low time requirements, CPU can even be used for inference.

Self-developed CBCT Reconstruction Algorithm

Fast Reconstruction Speed

Micron-Level Reconstruction Precision

Based on self-trained UNET denoising model, not super-resolution, no distortion

Super-resolution based on generative GAN network, may cause distortion

Real-time performance

Also supports super-resolution