| dc.description.abstract | With the progress of the times, science and technology change rapidly, more and more products use electronic components. For mass production, automated production methods have become the trend of the times. Almost all
electronic component products use printed circuit boards (PCBs), and there are large and small electronic components of different shapes on the printed circuit board. If these tiny electronic components use manual search, it will consume a lot of manpower and time costs, so it is an inevitable trend to replace manual search with machines. The automatic search method can make the components to be searched into a template image, and then use the matching technology to search for the same components in the large image to be tested.
In the past, there have been many related studies on feature matching of printed circuit boards, but most of them are traditional algorithms, which cannot effectively detect different types of electronic components, and are also
easily affected by component variation and background. Therefore, in this study, we propose a convolutional neural network-based matching network to remedy the above shortcomings. By inputting an image of the same electronic
component type as a template image, and searching for a matching component on the search image. Matching only needs to detect whether there is a part of the search image that matches the template image, and does not need to learn
the types of electronic components, which can be widely used.
In this study, we modify the SiamCAR architecture of a single tracking network as our matching network. The main modifications include: i. Change single target tracking to matching of multiple similar objects; ii. Improvement
of loss function. In addition, in training, we also add additional processing such as image preprocessing and learning rate strategy.
In the experiment, we collected 851 images of electronic components, including 794 in the training set, with 4,601 pairs of images, and 57 in the validation set, with 291 pairs of images. We tested with 1,200×1,200 resolution images, and the final improved precision is 98.43%, and the recall is 96.01%.Compared with the original SiamCAR network, the precision increased from 84.81% to 98.33%, an increase of 13.52%, and the recall increased from 66.02% to 94.60%, an increase of 28.58%. | en_US |