信息智能化技術的快速發展，對于貼片電容等電子元器件的需求量有了大幅度的提升，如何保證電容等電子類產品的安全性就需要對產品進行檢測，提高企業批量生產的質量和效率。因此需要AI視覺通過無接觸、無損傷的實時檢測方法代替人工、傳統方式檢測，從而提升企業的生產率及產品質量。

　　With the rapid development of information intelligent technology, the demand for electronic components such as chip capacitors has been greatly improved. How to ensure the safety of capacitors and other electronic products needs to be tested to improve the quality and efficiency of mass production of enterprises. Therefore, it is necessary for AI vision to replace manual and traditional detection methods by non-contact and non-invasive real-time detection method, so as to improve the productivity and product quality of enterprises.

　　像薄膜電容、貼片電容、電解電容等電子元器件生產過程中需要經過復雜的工藝處理，在多重工序處理下，會出現各種問題，如表面缺陷、字符不清等。因為電子元器件種類繁多，各類電子元器件的結構形狀、損壞程度和檢驗方法也均不相同，這就需要智能化的視覺檢測技術根據電容型號及特點進行定制化的檢測。

　　Such as film capacitors, chip capacitors, electrolytic capacitors and other electronic components in the production process need to go through a complex process, in the multi process processing, there will be a variety of problems, such as surface defects, unclear characters, etc. Because of the wide variety of electronic components, the structural shape, damage degree and inspection methods of various electronic components are different, which requires intelligent visual inspection technology to carry out customized detection according to the capacitor model and characteristics.

　　貼片電容元器件在生產過程中易出現孔洞、剝落、污點等缺陷，由于缺陷小，傳統算法需要耗費大量的時間對缺陷進行定制化開發，并且在進行灰度閾值分割時，易將微小的缺陷分割出去，很難保證在高速生產線上實現零缺陷檢測的要求；

　　Due to the small defects, the traditional algorithm needs a lot of time to develop customized defects, and when gray threshold segmentation is carried out, it is easy to separate the tiny defects, which is difficult to ensure the zero defect detection requirements in high-speed production line;

　　PCB板上存在很多焊點和細小零件，字符識別采集圖像時背景較為復雜，干擾因素多，造成字符定位和識別不準確，加上零件本身反光，會出現識別信息不全、誤識別以及識別速度慢等情況，無法滿足實際生產檢測過程中對PCB板字符識別的需求；

　　There are many solder joints and small parts on the PCB board. The background of character recognition image acquisition is complex, and there are many interference factors, which lead to inaccurate character positioning and recognition. In addition, the part itself reflects light, resulting in incomplete recognition information, false recognition and slow recognition speed, which can not meet the requirements of PCB character recognition in the actual production and detection process;

　　PCB板在焊接元器件過程中，需要檢測每個元器件位置是否正確、元器件是否缺失等情況，傳統算法無法對多種電子元器件定位識別，而且定制開發需要耗費大量的時間，容易受外界因素影響，導致錯誤定位或元器件缺失，直接影響PCB板的性能及生命周期。

　　In the process of PCB welding components, it is necessary to detect whether the position of each component is correct and whether the components are missing. The traditional algorithm can not locate and identify various electronic components, and the customized development needs a lot of time, which is easy to be affected by external factors, resulting in wrong positioning or missing components, which directly affects the performance and life cycle of PCB board.

　　貼片電容元器件缺陷檢測系統只需在線上傳不同缺陷數據圖片進行標注訓練，即可準確提取微米（μm）級的缺陷進行識別定位，從而實現高速流水線上零缺陷的目標；通過專屬的神經網絡架構，通過準確的標注訓練，完美適應復雜背景下的字符識別，識別率高達99.99%。

　　The defect detection system of SMD capacitor components can accurately extract micron (μ m) defects for identification and positioning by uploading different defect data pictures online for annotation training, so as to achieve the goal of zero defect on high-speed pipeline; through the exclusive neural network architecture and accurate annotation training, it is perfectly adapted to character recognition under complex background, and the recognition rate is as high as 99.9 9%。

　　對不同的類型的貼片電容元器件進行定制化開發，只需上傳合格的產品圖片，對圖片內的元器件進行訓練學習，即可準確定位PCB板上的元器件位置及完整性，在復雜的場景下擁有更好的效果，識別速度可達毫秒級別。

　　For the customized development of different types of SMD capacitor components, we only need to upload qualified product pictures and train and learn the components in the pictures, then we can accurately locate the position and integrity of components on PCB board, and have better effect in complex scenes, and the recognition speed can reach millisecond level.