Transfer Learning-Driven CNN Architectures for Automated Defect Detection in Printed Circuit Boards

Le résumé fourni par la source

Recent innovations in electronics manufacturing have created demands for timely an accurate defect during production of printed circuit board (PCB). The following paper gives a solid approach to PCBA defect detection using CNN, especially by transfer learning. In this method, the experts used pre-trained models like ResNet and EfficientNet besides retraining them on PCB defect datasets that are available to the public; this boosted the defect recognition process greatly. The method involves using TensorFlow/Keras based frameworks in the implementation and optimization of the CNN models and use OpenCV for a broad image preprocessing. Said integration of transfer learning with current most advanced neural architectures enables the achievement of very high detection rates even where domain-specific information is scarce. This study reveals the promising idea of using open datasets and developing practical CNN approaches to detect the defects for scaled electronics manufacturing.

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