Hybrid CNN with transfer learning and MobileNetV2 for advanced multi-class PCB defect detection

Printed Circuit Board (PCB) plays an important role in the world of electronics. Regarding PCBs, manufacturing defects not only worsen the product qualification rate but can also lead to catastrophic failure of the electronic devices themselves. This study introduces a new model to accurately and efficiently detect different types of PCB defects, including spur, open circuit, short, mouse bite, missing hole, and spurious copper. The proposed model presents and then overcomes challenges in detecting PCB defects using a dense layer in a Convolutional Neural Network and advanced digital image processing and augmentation techniques such as contrast, scaling, and rotation. This study is based on the MobileNetV2 framework in proposing a hybrid Convolutional Neural Network scheme that combines the strength of convolutional feature extraction with the beneficial reorganization of features by fully connected layers to enable accurate and efficient detection of common Printed Circuit Board defects. The hybrid Convolutional Neural Network is responsible for classification, while feature extraction is performed through MobileNetV2. The results certify that the proposed model achieves an accuracy of 96%. Moreover, ROC curves provide an AUC measure higher than 0.99 for all types of defects. Comparative results show a substantial improvement in performance over traditional models.