School of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao 266525, China.
Sensors (Basel). 2020 Sep 17;20(18):5318. doi: 10.3390/s20185318.
Locating and identifying the components mounted on a printed circuit board (PCB) based on machine vision is an important and challenging problem for automated PCB inspection and automated PCB recycling. In this paper, we propose a PCB semantic segmentation method based on depth images that segments and recognizes components in the PCB through pixel classification. The image training set for the PCB was automatically synthesized with graphic rendering. Based on a series of concentric circles centered at the given depth pixel, we extracted the depth difference features from the depth images in the training set to train a random forest pixel classifier. By using the constructed random forest pixel classifier, we performed semantic segmentation for the PCB to segment and recognize components in the PCB through pixel classification. Experiments on both synthetic and real test sets were conducted to verify the effectiveness of the proposed method. The experimental results demonstrate that our method can segment and recognize most of the components from a real depth image of the PCB. Our method is immune to illumination changes and can be implemented in parallel on a GPU.
基于机器视觉的印刷电路板（PCB）上元件的定位和识别是自动化 PCB 检测和自动化 PCB 回收的一个重要且具有挑战性的问题。在本文中，我们提出了一种基于深度图像的 PCB 语义分割方法，该方法通过像素分类对 PCB 中的元件进行分割和识别。通过图形渲染自动合成 PCB 的图像训练集。基于以给定深度像素为中心的一系列同心圆，我们从训练集中的深度图像中提取深度差特征，以训练随机森林像素分类器。通过使用构建的随机森林像素分类器，我们对 PCB 进行语义分割，通过像素分类对 PCB 中的元件进行分割和识别。在合成和真实测试集上进行了实验，以验证所提出方法的有效性。实验结果表明，我们的方法可以从 PCB 的真实深度图像中分割和识别大部分元件。我们的方法对光照变化具有免疫力，并且可以在 GPU 上并行实现。
