| 摘要(英) |
The printed circuit board (PCB) industry of Taiwan is extremely competitive in the world. The PCB’s applications are quite extensive; almost all electronic components are soldered on the PCBs to work. A soldered PCB is called a printed circuit board assembly (PCBA). The market of PCBA products is very fierce; in order to keep the product advantages, product quality control is very important. To prevent defective products being delivered to consumers, defect detection of PCBAs has become an important issue.
In the traditional detection methods, we always need to design specific algorithms for special components to obtain better detection results. Although the detection speed is very fast, the disadvantage is that the generalization is very low. If unobserved components are appeared, algorithms must be redesigned. In recent years, deep learning techniques have been developed vigorously and have revealed that they can effectively improve the performance of variant applications. In this study, we use the deep learning technique to compare the similarity between the electronic components on a test PCB and the related master PCB to find defect components on the test PCB. The similarity criterion is hard to define; for example, different components, different color, large rotation, and large shift are defects; little change on brightness, color, rotation, shift, and background change are not defect. Although the criterion is hard to define from the traditional concept, using learning methodology from training samples to generate the criterion is very practical.
In this study, we modified the Siamese network to produce our comparison network; the main modification includes: i. the feature extraction subnet was changed to ResNet-18; ii. ResNet-18 was re-organized as a pre-activation architecture; iii. adding attention modules to improve the learning effect; iv. based on the special requirements, location-related features are defined and used to compare the similarity; v. a new loss function is proposed to match the special features. Moreover, extra processes such as contrast enhancement, data augmentation, learning rate strategies, and random shift are made in training.
In the experiments, we collected 3,528 pairs of electronic component images on PCBs as samples; in which, 1,697 pairs are similar images and 1,831 pairs are dissimilar images. Among them, 1,547- and 150-pair similar images are respectively for training and verification; 1,681- and 150-pair dissimilar images are respectively for training and verification. All images were augmented into 5 times to generate totally 16,140 training pairs and 1,500 validation pairs.
The experimental results show that the precision of the training set of the feature extraction subnet using the original ResNet-18 is 78.88%, the recall is 62.70%, and the precision of the validation set is 84.32%, the recall is 64.53%. In the environment of using a new loss function with a new feature comparison metric and adding image pre-processing, data augmentation, learning rate strategies, and random displacement images. After the neural network architecture was modified and modules were added, the final precision of the training set reached 100.00%, the recall reached 100.00%, and the final precision of the verification set reached 100.00%, the recall reached 99.33%. |
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