信息智能化技术的快速发展，对于贴片电容等电子元器件的需求量有了大幅度的提升，如何保证电容等电子类产品的安全性就需要对产品进行检测，提高企业批量生产的质量和效率。因此需要AI视觉通过无接触、无损伤的实时检测方法代替人工、传统方式检测，从而提升企业的生产率及产品质量。

　　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.

　　像薄膜电容、贴片电容、电解电容等电子元器件生产过程中需要经过复杂的工艺处理，在多重工序处理下，会出现各种问题，如表面缺陷、字符不清等。因为电子元器件种类繁多，各类电子元器件的结构形状、损坏程度和检验方法也均不相同，这就需要智能化的视觉检测技术根据电容型号及特点进行定制化的检测。

　　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.

　　贴片电容元器件在生产过程中易出现孔洞、剥落、污点等缺陷，由于缺陷小，传统算法需要耗费大量的时间对缺陷进行定制化开发，并且在进行灰度阈值分割时，易将微小的缺陷分割出去，很难保证在高速生产线上实现零缺陷检测的要求；

　　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板上存在很多焊点和细小零件，字符识别采集图像时背景较为复杂，干扰因素多，造成字符定位和识别不准确，加上零件本身反光，会出现识别信息不全、误识别以及识别速度慢等情况，无法满足实际生产检测过程中对PCB板字符识别的需求；

　　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板在焊接元器件过程中，需要检测每个元器件位置是否正确、元器件是否缺失等情况，传统算法无法对多种电子元器件定位识别，而且定制开发需要耗费大量的时间，容易受外界因素影响，导致错误定位或元器件缺失，直接影响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.

　　贴片电容元器件缺陷检测系统只需在线上传不同缺陷数据图片进行标注训练，即可准确提取微米（μm）级的缺陷进行识别定位，从而实现高速流水线上零缺陷的目标；通过专属的神经网络架构，通过准确的标注训练，完美适应复杂背景下的字符识别，识别率高达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%。

　　对不同的类型的贴片电容元器件进行定制化开发，只需上传合格的产品图片，对图片内的元器件进行训练学习，即可准确定位PCB板上的元器件位置及完整性，在复杂的场景下拥有更好的效果，识别速度可达毫秒级别。

　　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.