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打破传统设备的壁垒:AI技术与机器视觉检测系统完美结合

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  机器视觉工业产业链的深度布局进一步丰富产品线和产能建设,加强人工智能技术在机器视觉检测系统工业检测领域应用的研发。
  The deep layout of the industrial chain of machine vision industry further enriches the product line and capacity construction, and strengthens the research and development of artificial intelligence technology in the field of machine vision industrial inspection.
  传统的检测设备,完全依靠技术方设定的算法数据库,只能重复性地检出已知的已输入的缺陷类型,对于新的、不常见的缺陷却无法识别检出。
  The traditional detection equipment, completely relying on the algorithm database set by the technical side, can only repeatedly detect known types of defects that have been input, but can not identify and detect new and uncommon defects.
  通过重新把缺陷的定义过程交还给一线员工,一线员工仅需对缺陷进行初步的标记分类,设备就会“记住”这些判定,并模拟人脑进行分析识别,随着检测数据量的不断增加,数据库的不断扩大,检测结果将越加精准。
  By redefining the process of defect definition to the front-line staff, the front-line staff only need to carry out preliminary mark classification for defects, and the equipment will "remember" these judgments, and simulate the human brain for analysis and identification. With the increasing amount of detection data and the continuous expansion of the database, the detection results will be more accurate.
AI技术融入到机器视觉检测领域
  在应用案例上,将AI技术融入到晶硅电池视觉检测中,打破了传统自动化设备的壁垒,实现真正的智能化、数据化;目前,除了晶硅电池行业,利珀科技也将人工智能技术运用到了其他视觉检测领域,AI技术的应用是缺陷检测行业不可避免的趋势。
  In the application case, AI technology is integrated into the visual inspection of crystalline silicon battery, which breaks the barriers of traditional automation equipment and realizes real intelligence and data. At present, in addition to the crystalline silicon battery industry, Lipper technology also applies artificial intelligence technology to other visual inspection fields, and the application of AI technology is an inevitable trend in the defect detection industry.
  通用机器视觉开发平台,将数千个算子组合成了近百个核心的算法工具,用户无需编写任何代码,只需要过“拖”、“拉”、“点”将各种算法工具进行组合,就可以实现各种视觉检测任务,简化机器视觉系统实现的复杂度,解决了项目开发周期长、人力物力成本高的行业痛点。
  The general machine vision development platform combines thousands of operators into nearly one hundred core algorithm tools. Users do not need to write any code, but only need to combine various algorithms and tools through "drag", "pull" and "point", so as to realize various visual inspection tasks, simplify the complexity of machine vision system implementation, and solve the problem of long project development cycle and human and material resources cost High industry pain points.
  机器视觉作为让机器人等自动化设备更加智能化的技术在这些年广受资本市场青睐,即便在资本“荒”的特殊时期,机器视觉仍然保持着较高的关注度,企业融资事件络绎不绝。
  Machine vision, as a technology to make robots and other automation equipment more intelligent, has been widely favored by the capital market in recent years. Even in the special period of capital shortage, machine vision still maintains a high degree of attention, and enterprise financing events continue to flow.
  未来,在AI技术应用到机器视觉检测系统的深度和宽度上做更大的突破,是可以将人的学识和经验传授给设备的工具,让机器人看懂制造,实现真正的机器自动化的理想。
  In the future, a greater breakthrough in the depth and width of AI technology applied to machine vision inspection system is a tool that can impart human knowledge and experience to equipment, so that robots can understand manufacturing and realize the ideal of real machine automation.