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.

中文翻译：

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基于深度图像的用于组件识别的印刷电路板的语义分割。

基于机器视觉来定位和识别安装在印刷电路板（PCB）上的组件对于自动化PCB检查和自动化PCB回收而言是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中的组件进行分割和识别。PCB的图像训练集通过图形渲染自动合成。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了深度差特征，以训练随机森林像素分类器。通过使用构造的随机森林像素分类器，我们对PCB进行了语义分割，以通过像素分类对PCB中的组件进行分割和识别。进行了综合测试集和真实测试集的实验，以验证该方法的有效性。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多数组件。我们的方法不受光照变化的影响，可以在GPU上并行实现。