Based on the ManuVision Intelligent Machine Vision Platform, it has effectively resolved gaps in the missing data samples and imageries undermined by factors such as temperature, humidity, geological structure. The automated assessment of indicators, such as voids, thickness and number of reinforcements, reduces manual interventions and improves inspection efficiency.

We combined full-process automation equipment with deep learning technology to achieve automated completion of the production process from pixel matrix to packaging and testing, and established a complete monitoring system.

Our project created the world’s first intelligent molten iron transportation system based on artificial intelligence vision algorithm and automatic driving technology. It helps locomotives and torpedo tank cars to automatically detect and avoid obstacles on the track, identify locomotive tails and hooks, and achieve fully automated hook removal.

Employing computer vision algorithms and prediction technology, it is equipped with the ability to analyze the monitoring videos and combines the traditional video monitoring and on-site inspection to automate detection of abnormality and alerts.

Leveraging our ManuVision Intelligent Machine Vision Platform, we developed two intelligent inspection devices that monitored product assembly in real time to resolve the issues in each situation. One of the devices is aimed to detect whether the timing points are aligned, and the other ensured the retaining rings for piston pins were assembled correctly.

Based on our ManuVision Intelligent Machine Vision Platform, our deep learning and computer vision technologies were paired with industrial-grade high resolution camera and lighting equipment to perform automated inspection process, and continue to refine its detection capabilities，and ensured an accurate detection and classification of defects .