Fully self-developed — from algorithm engine to line integration
A fully self-developed, full-stack industrial vision algorithm platform — not a simple integration of open-source tools, but an engineering asset refined through long-term iteration on more than a hundred complex production line projects.
The platform ships with 100+ highly modular components covering positioning and matching, semantic segmentation, surface defect detection, high-precision dimensional measurement and 3D reconstruction. It supports hybrid modeling of 1D/2D/3D multimodal data with deep learning models, forming a reusable, traceable and scalable paradigm for vision application development.
Key strengths include a highly standardized component library, strong cross-scenario reuse, extreme stability and very high execution efficiency — capable of supporting high-tact and highly complex industrial inspection and smart manufacturing scenarios.
Self-developed with one-stop labeling / training / evaluation / testing services. Our “global model + local fine-tuning + lightweight deployment” approach builds a leading industrial-grade deep learning stack.
By deeply integrating mainstream model technologies in industrial vision, we have built a unified cross-task stack with seven core capabilities modularized: semantic segmentation / instance segmentation / rotated object detection / object detection / OCR / unsupervised / classification.
Models pre-trained on tens of thousands of industrial images improve inference accuracy by 12–35%.
Lightweight deployment: structural pruning plus quantized compilation compresses model size by 80%, balancing accuracy and line tact time.
Leave your product type, line speed and main defect types — we will propose a tailored inspection solution and configuration.
