Surface defect prediction on printed circuit boards using a novel deep learning model with spatial and channel attention-based DenseNet

The electronic components are connected using printed circuit boards (PCBs), which is the most essential stage in electronic product manufacturing. It makes the final product inoperable when there is a minor defect in the PCB. Hence, in the manufacturing process of PCB, careful and meticulous defect detection stages are essential and indispensable. An optimal deep learning (DL) system with an effective pre-trained feature learning mechanism is proposed in this paper to find out PCB’s surface defects. The system primarily performs preprocessing that includes contrast enhancement by contrast-limited adaptive histogram equalization (CLAHE) and noise removal by adaptive median filter (AMF) to enhance the contrast of the images and suppress the noise present in the image. Then, the class imbalance problem is solved by using the k-means synthetic minority over-sampling technique (KM-SMOTE). After that, the important discriminative features are extracted by using the spatial and channel attention-based DenseNet-21 (SCDSNT121). Finally, the defect classes are classified by using the reptile optimized gated recurrent unit (ROGRU). The six classes of PCB images are trained from a publicly available deep PCB dataset, and the system achieved the results with an accuracy of 99.12%.