A Deep Learning-Based System for Detecting Defects in Printed Circuit Boards

Traditional approaches to defect detection, relying on rule-based algorithms or human visual examination, are inadequate and ineffectual in handling intricate fault patterns. Implementing computer vision techniques in PCB defect detection can improve inspection processes' accuracy, reliability, and effectiveness. Deep learning is now used in those processes to explore important technology. This study presents a novel automated method for defect identification using deep learning methods. The system is built utilizing AlexNet, U-Net, ResNet 101, and Inception-v3, prominent frameworks known for their outstanding capacity to extract pertinent visual data components. The proposed technique comprehensively composes PCB photos, including faulty and defect-free samples. This training enhances the system's capacity to precisely differentiate between conventional PCB designs and six additional categories of errors. The suggested architecture of the CNN models consists of double process layers represented as ConV I, Pooling I, ConV II, and Pooling II. The validation procedure is carried out by using a range of learning rate data. The implementation findings indicate that the ResNet-101 variant achieves the highest testing accuracy of 99.286%. Indeed, incorporating this technology into the PCB manufacturing process enables the prompt identification of defective PCBs, leading to less waste, enhanced product quality, and heightened satisfaction among end-users.