精密元器件工业生产自动化检测的算法研究

Research on Algorithm for Automatic Detection of Precision Components in Industrial Production

在精密元器件的生产过程中,对产品进行缺陷检测是至关重要的一个步骤,同时缺陷检测也是计算机视觉领域一个重点研究内容。文章算法研究使用Python作为编程语言,对图片数据进行预处理以及数据增强后,使用YOLO(You Only Look Once:Unified,Real-Time Object Detection)和Faster R-CNN(Towards Real-Time Object Detection with Region Proposal Networks)模型对图片数据进行训练,同时构建一个高性能的轻量化特征提取网络,实现电子元器件的快速特征提取。

In the production process of precision components,product defect detection is a crucial step,and defect detection is also a key research content in the field of computer vision.This algorithm research uses Python as the programming language to preprocess the image data and enhance the data,then uses YOLO(You Only Look Once:Unified,Real-Time Object Detection)and Faster R-CNN(Towards Real-Time Object Detection with Region Proposal Networks)models to train image data.At the same time,it builds a lightweight feature extraction network with high performance to achieve rapid feature extraction of electronic components.