Unsupervised Defect Detection in Automotive Quality Inspection with Convolutional Autoencoder

This paper introduces an unsupervised defect detection system using a Convolutional Autoencoder (CAE) for brake caliper quality control in automotive manufacturing. A CAE is trained to reconstruct defect-free images, leveraging a Structural Similarity Index Measure (SSIM) loss to isolate anomalies between original and reconstructed images. Candidate defect regions are refined using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering to distinguish true defects (e.g., deformities, scratches) from noise. Experiments conducted on a custom imaging workstation demonstrated strong performance in diverse regions of interest (ROIs) of the brake calipers. The system achieved F1-scores of 0.92, 0.95, and 0.76 on the logo, flat (2D), and non-flat (3D) ROIs, respectively. Data augmentation improved generalization, and clustering reduced false positives (FPs). By offering an alternative to traditional supervised methods, this CAE-based approach enables reliable defect detection, reducing manual dependence. Its flexible design supports integration into automotive production, leading to improved real-time monitoring and cost savings.