电子元器件缺陷检测模型的自动训练系统

深度学习方法可提高AOI的速度和精度，但因实际工业生产中AOI场景多变，模型需不断更新以保证性能，耗时长，人力成本高。为了提高实际AOI中深度学习模型的迭代效率，研发了一套面向PCBA贴片电子元器件的缺陷检测模型自动训练系统，对常见的四类电子元器件(Chip、IC、SOT、排插)所需的缺陷检测模型实现了自动训练，自动训练过程分为自动数据增强、自动调参与自动部署3个部分。实验结果表明，该系统自动训练得到的模型性能总体优于人工手动训练的模型，相较人工手动训练，训练耗时缩短36%~42%，整体准确率提升1.3%~4.1%。目前该系统已经完成测试，自动训练出的模型能满足实际AOI的检测要求，有效提高了模型迭代速度，减少了人力成本，具有较好的应用前景。

Automatic training system of electronic component defect detection model

Deep learning methods can improve the speed and accuracy of AOI. However, due to the variable nature of industrial production scenarios, the models have to be updated continuously to ensure performance, which increases time and labor consumption. In order to improve the model iteration efficiency of deep learning methods in actual practice of AOI, an automatic training system for defect detection models of PCBA electronic components is developed in this paper. The system can automatically train the defect detection models required for four common types of electronic components(Chip, IC, SOT, and Plug-in). The automatic training process is divided into three parts: automatic data enhancement, parameter tuning, and deployment. Experimental results show that the models automatically trained by the system outperform the manual training models. Compared with manual training, the training time is shortened by 36% to 42%, and the overall accuracy is increased by 1.3% to 4.1%. At present, the proposed system has completed testing and the automatically trained model can meet the requirements of actual practice of AOI. It effectively improves the speed of model iteration, reduces labor costs and demonstrates its good application prospects.