Abstract
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.
Highlights
The printed circuit board (PCB) is an important part of modern electronic products
We propose a PCB semantic segmentation method based on depth images, to segment and recognize components in the PCB through pixel classification
The synthetic test set is composed of 40 depth images that were randomly selected from the synthetic subset and a real test subset
Summary
The printed circuit board (PCB) is an important part of modern electronic products. The quality of the PCB determines the quality of the product. Herchenbach et al [14] proposed a framework for segmentation and classification of through-hole components on PCBs to enable the automatic recycling of PCBs by using both RGB (Red Green Blue) and depth frames from the Microsoft Kinect sensor as input They used a multi-threshold approach to create segmentation hypotheses of components and searched for the best hypothesis using an optimization step. We propose a PCB semantic segmentation method based on depth images, to segment and recognize components in the PCB through pixel classification. A random forest pixel classifier based on depth images is applied to the semantic segmentation of a PCB, which segments and recognizes the components in the PCB through pixel classification. A method for segmenting and recognizing components on a PCB is proposed by using a random forest pixel classifier based on depth images.
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