An Integrated Hybrid Framework for Crack Detection on Pressed Panels with Baseline-Driven False-Positive Suppression
Geon Park, Yeseul Kong, Penghua Zhang, Gyuhae Park
TL;DR: A baseline-panel–driven correction map that utilizes prior information extracted from healthy panels is introduced that enhances the stability and accuracy of crack detection, enabling more reliable inspection under the challenging lighting conditions of real automotive press lines.
Crack detection on pressed panels is essential for maintaining quality in automotive manufacturing. However, achieving stable inspection is challenging due to strong reflections on metallic surfaces, geometric curvature, and varying illumination conditions. To address these issues, this study proposes a hybrid approach that combines shape-based analysis with unsupervised deep learning. Shape-based analysis leverages the panel’s edge geometry and therefore remains relatively robust to illumination changes, while unsupervised models learn the normal appearance of panels and perform anomaly detection without requiring defect labels. Despite these advantages, both methods commonly suffer from over-detection, where structural edges or reflection patterns are misinterpreted as cracks. To mitigate this problem, we introduce a baseline-panel–driven correction map that utilizes prior information extracted from healthy panels. By capturing recurring geometric features and reflection patterns, this correction step effectively suppresses systematic false positives that repeatedly occur in the same regions. When integrated with shape analysis and unsupervised learning, the proposed correction mechanism enhances the stability and accuracy of crack detection, enabling more reliable inspection under the challenging lighting conditions of real automotive press lines.