Performance Analysis of Printed Circuit Board Defect Detection with Hybrid CNN Module Image Feature Extraction and Clustering

Accurate and efficient defect detection in printed circuit boards (PCBs) is critical for manufacturing quality control. Existing methods predominantly rely on manually extracted features such as surface texture, color, and shape for defect recognition and classification within small-dimensional feature datasets. A convolutional neural network (CNN) model was developed via transfer learning. Feature extraction involves diverse operations across different CNN layers. Essential features were selected, and dimensionality was reduced via either t-distributed stochastic neighbor embedding (t-SNE) or principal component analysis (PCA). Defect classification was subsequently performed by clustering the reduced features with either the K-means or K-nearest neighbors (KNN) algorithm. Compared with alternative model feature learning classifiers, the proposed small-dimensional CNN model performs significantly better. A defect recognition accuracy of 97.33% was achieved, with processing completed in approximately 60 s. This approach, which integrates transfer learning-based CNN feature extraction with dimensionality reduction and clustering techniques, provides a fast and effective method for high-precision defect detection and classification in PCBs.