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系統(tǒng)基于深度學習技術，訓練具有缺陷分類和分級功能的深度神經(jīng)網(wǎng)絡，利用神經(jīng)網(wǎng)絡自動對清分機、碼后核查檢出的作廢產(chǎn)品進行判廢，再對已經(jīng)判廢的產(chǎn)品進行智能分類，將缺陷歸類到各自工序、不同的缺陷類型進行準確歸集。實現(xiàn)降低人工判廢的工作量，代替人工重復勞動的目的，提升判廢可靠性。

The intelligent detection system boasts cutting-edge high-brightness cold light source color imaging technology, high-speed true color image processing technology, and real-time synchronous control technology to enable rapid quality detection of printing products. It is designed for installation on the process and final detection equipment like offset presses, gravure presses, inkjet printers, paper machines, paper cutters, coaters, and banknote processing machines, and can utilize deep learning technology to manage printing quality across different processes online and in real time. The intelligent detection system tackles challenges like paper deformation and imaging shadows in gravure printing processes, significantly reducing false alarms from the system. Moreover, the system stands out by offering simplified modeling, unified template management, and consistent detection performance.

The system applies to a range of products like invoices, receipts, express bills, labels, and checks. It offers customization options to address specific anti-counterfeiting requirements by adding elements such as infrared, fluorescent features, safety threads, holographic labels, colored fibre filaments, and other anti-counterfeiting measures. Ensuring comprehensive detection across each small product, it provides operators with a quality observation interface to identify and intercept continuous defects in real time. This effectively reduces product loss. The system is adaptable for installation on various continuous paper printing devices, including rotary printing presses, flexographic printers, and web gravure presses. These devices encompass both international brands like Heidelberger Druckmaschinen Aktiengesellschaft, Müller Martini, Roland DGA Corporation, Koenig & Bauer AG, and Komori, as well as domestic models from Beiren and Taiyang.