PCB Defect Detection Using Machine Learning: YOLOv5 and Inception v3 CNN-Based Approach

Identification of PCB board defects early in the manufacturing process is crucial, as PCB quality control plays an important role in the electronics manufacturing industry. Defective PCBs can lead to product failures, increased costs, and reliability issues. The idea proposes a machine learning program for the detection and classification of defects in PCBs using a YOLOv5 object detection algorithm and an Inception v3 CNNbased approach. The dataset consists of annotated PCB images for the YOLOv5 algorithm, and the images are categorized into six defect types: missing holes, mouse bite, open circuit, short circuit, spurs, and spurious copper. The preprocessed defect images are analyzed using the YOLOv5 model for defect location, while the Inception v3 CNN model performs precise defect classification. The results and the output clearly show that the proposed approach achieves high accuracy in both detection and classification tasks. Hence, the aim is to provide an efficient and automated solution for PCB inspection, outperforming conventional manual checks.