Abstract
Printed Circuit Boards (PCBs) are very important for proper functioning of any electronic device. PCBs are installed in almost all the electronic device and their functionality is dependent on the perfection of PCBs. If PCBs do not function properly then the whole electric machine might fail. So, keeping this in mind researchers are working in this field to develop error free PCBs. Initially these PCBs were examined by the human beings manually, but the human error did not give good results as sometime defected PCBs were categorized as non-defective. So, researchers and experts transformed this manual traditional examination to automated systems. Further to this research image processing and computer vision came into actions where the computer vision experts applied image processing techniques to extract the defects. But, this also did not yield good results. So, to further explore this area Machine Learning and Artificial Intelligence Techniques were applied. In this study we have applied Deep Neural Networks to detect the defects in the PCBS. Pretrained VGG16 and Inception networks were applied to extract the relevant features. DeepPCB dataset was used in this study, it has 1500 pairs of both defected and non-defected images. Image pre-processing and data augmentation techniques were applied to increase the training set. Convolution neural networks were applied to classify the test data. The results were compared with state-of-the art technique and it proved that the proposed methodology outperformed it. Performance evaluation metrics were applied to evaluate the proposed methodology. Precision 94.11%, Recall 89.23%, F-Measure 91.91%, and Accuracy 92.67%.
Highlights
Printed circuit boards (PCB) are included in almost all the electronic devices and it plays an important role in proper functioning of these devices
The dataset used in this work for the training of proposed methodology and development of the model is DeepPCB
The following are the parameters in these equations: True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN)
Summary
Printed circuit boards (PCB) are included in almost all the electronic devices and it plays an important role in proper functioning of these devices. To overcome the errors and increase the defect detection accuracy image processing techniques are widely applied in PCBs manufacturing companies [8]. These image processing techniques can locate the parts in PCBs where the defects have occurred and further classify them Still they are some limitations in these techniques and to overcome those machine learning techniques are applied in the very recent times [7]. In the current time machine learning is applied extensively, especially in solve image processing problems because of its ability to automatically produce discriminative features These features are developed with little training samples with the help of introducing learning and pattern recognition algorithms. Machine learning based techniques which is both accurate and fast in detecting the defects in PCB images are introduced with the help of deep learning techniques. There are so many methodologies applied but convolution neural network (CNN) is applied in many applications like [29,30], where the core idea is image recognition for the detection of objects
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