A vision inspection machine uses industrial cameras, lighting, image processing software and AI technology to automatically inspect products and identify defects or abnormalities.

We are a manufacturer of visual inspection equipment  which can offer flexible customized solutions and faster after-sales support compared to trading company.

We integrate independent R&D, intelligent manufacturing and full-scene after-sales service, owning a complete industrial visual inspection industrial chain. Different from ordinary manufacturers with single equipment production, we have self-developed AI algorithm platforms, independent optical laboratory and standardized assembly workshops. We adopt strict quality control from raw material procurement, precision processing to finished product debugging, ensuring stable equipment consistency and ultra-low failure rate. In addition, our flexible customization capability, fast delivery cycle and cost-effective pricing without middleman markup help clients reduce quality inspection costs and improve production efficiency comprehensively.

Traditional visual inspection relies on fixed rule algorithms, which can only identify preset defect types, with poor adaptability to new and unknown defects, and requires repeated parameter adjustment for different products. Our industrial AI large model adopts pre-trained universal visual model + small sample fine-tuning technology. It has strong cross-domain generalization ability, can identify unknown, irregular and complex defects without full preset rules, and effectively solves the pain points of high missing inspection rate and poor compatibility of traditional equipment. It upgrades from "fixed-rule detection" to "intelligent understanding detection".

Firstly, it supports small-sample and even single-sample rapid modeling, which shortens the algorithm deployment cycle from months to weeks or even days, greatly reducing customer debugging time. Secondly, it has ultra-high inspection accuracy, with an industrial defect recognition accuracy rate of over 99.9%, stable all-day operation without fatigue. Thirdly, it supports autonomous iteration. The model can continuously learn and optimize according to on-site production data, automatically adapt to product process changes. Fourthly, it supports cloud-edge collaborative operation, with cloud large model processing complex defect analysis and edge lightweight model completing real-time high-speed detection, balancing accuracy and efficiency.

Our upgraded industrial AI large model supports ultra-few-shot learning, which is far more efficient than traditional machine vision algorithms. For conventional common defects, only 20–50 qualified and defective sample images are required for stable model training and effective recognition. For simple single defect scenarios, accurate modeling can be completed with only 5–10 sample sets. For complex, irregular and multi-type mixed defects, 100–200 diverse samples are sufficient to complete stable training. The model supports continuous incremental learning, and new defect samples can be added at any time to optimize the recognition effect without retraining the entire model.