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 P...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 studywe have appliedDeep Neural Networks to detect the defects in the PCBS.PretrainedVGG16and Inception networkswere 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%.展开更多
基金The author would like to thank Deanship of Scientific Research at Shaqra University for their support to carry this work.
文摘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 studywe have appliedDeep Neural Networks to detect the defects in the PCBS.PretrainedVGG16and Inception networkswere 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%.
