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AI深度學(xué)習復(fù)判系統(tǒng)解決方案

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AI深度學(xué)習復(fù)判系統(tǒng)解決方案

AI深度學(xué)習復(fù)判系統(tǒng)解決方案

 

   目前市場上現(xiàn)存的傳統(tǒng)AOI檢測設(shè)備大多不具備AI深度學(xué)習功能,普遍存在誤報率高等痛點,無法實現(xiàn)自動化生產(chǎn),生產(chǎn)效率低下。AI人工智能系統(tǒng)通過網(wǎng)絡(luò)與友商AOI設(shè)備通信,并在友商AOI設(shè)備檢測結(jié)果的基礎(chǔ)上,利用基于深度學(xué)習的AI人工智能系統(tǒng)進行復(fù)判,降低誤報率,提高檢測準確率。

 

 

 

特色方案
  • 節(jié)省人力成本

    減少人工復(fù)判的工作量,節(jié)省人力成本,實現(xiàn)自動化生產(chǎn),提高生產(chǎn)效率和效益。

  • 提高檢測準確率

    避免人工復(fù)判的不穩(wěn)定性,保證缺陷品的高效、穩(wěn)定檢出;

  • AOI+AI復(fù)判升級改造

    無需對產(chǎn)線進行硬件改造,對生產(chǎn)影響小,可實現(xiàn)快速接入。

     

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At present, most of the existing traditional AOI detection equipment in the market does not have AI deep learning function, and generally has pain points such as high false alarm rate, which cannot achieve automated production and low production efficiency. The AI artificial intelligence system communicates with friendly AOI devices through the network, and based on the detection results of friendly AOI devices, uses a deep learning based AI artificial intelligence system for re evaluation to reduce false alarm rates and improve detection accuracy.

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AI artificial intelligence system - particle count re evaluation system

The particle count re evaluation system is used to analyze the detection report data (particle count) of friendly particle indentation inspection machines. For products with particle counts less than the set threshold, traditional image algorithms and AI deep learning systems are used for secondary re evaluation, greatly improving the detection accuracy. The real NG data is uploaded through CIM and locked.

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