Deep Vision Is a young high education, high technology talent company.The core AI research and development team is composed of two oversea doctoral supervisors and many young doctors and masters from the United States and China.With Shenzhen as the headquarters and R&D center, the company has business and service distribution in South China (Shenzhen center) and East China (Suzhou center).

Team Structure
  • National high-tech enterprise
    South China Machine Vision Alliance Director Unit
  • Shenzhen high-tech enterprises
    Deep Vision Innovation AI Technology Research Base
  • Guangdong Multimedia Information Service Engineering Technology Research Center
    AI doctoral research base of Southern University of Science and Technology
  • Strategic cooperation unit of Suzhou Intelligent Manufacturing Research Institute
    Member of the Yangtze River Delta (Suzhou) Machine Vision Industry Innovation Alliance
Fast deep learning technology can greatly improve the speed without affecting the accuracy, and has the advantages of high efficiency, low cost and scale.Our technological breakthrough in AI deep learning chip has once again greatly reduced the cost for customers to use deep learning technology.In the near future to AI integration, product miniaturization to launch new products.
  • Fast matching and localization of repeated textures in industrial vision

  • A Visual Deep Learning Network Training Method for Defective Samples

  • Adaptive defect detection method for flexible circuit board

  • Automatic discrimination of similar and different products based on deep learning

  • Fast generic ROI matching method

  • Fast OCR recognition method based on deep learning network

  • Visual detection algorithm for glass defects

  • Shape matching algorithm based on annotation

  • Method for detecting template defects

  • A joint multi-channel product defect classification method based on deep learning

  • Defect Detection Method Based on Deep Neural Network Heat Map Prediction

  • Robust deep neural network learning method for sample labeling error

  • A method for automatic identification of real defects and overkill based on decision tree

  • Mass image annotation method

  • Detection method for camera module defects

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