An Improved Small Defect Classification System Using Image Patching Methodology on Aluminum Car Doors

Small surface defects in automotive manufacturing such as micro-cracks, springbacks, and minor deformations typically appear in highly curved or reflective regions of large and complex components. Although recent Artificial Intelligence (AI)–based visual inspection systems have improved classification, existing whole-image classification methods still struggle to identify subtle or localized defects that occupy only a small portion of the image. These subtle defects occupy only small and localized regions, analyzing the entire image as a single representation often leads to missed detections and insufficient sensitivity. To address these issues, we propose a small defect classification system using patch-based methodology. The approach divides each car-door image into spatial windows, enabling localized inspection of subtle defects that would otherwise be masked in full-image representations. First, Each window is processed using an enhanced Large Kernel Attention (LKA)-based ResNet50 backbone, where the LKA mechanism increases the effective receptive field and improves small-defect discriminability within each patch. Second, To overcome the limitation of independent patch predictions, we introduce a Self-Attention-Based Multi-Layer Perceptron (MLP) Aggregator that learns inter-patch dependencies and adaptively fuses window-level outputs into a robust whole-image decision, ensuring that cross-window defects are not overlooked. Experimental results showthat the combination of localized patch inspection, LKA-enhanced feature extraction, and self-attention based MLP aggregation yields superior performance, achieving 95% accuracy and 95% F1-score. These results confirm the effectiveness of the proposed image patching methodology as a reliable and scalable solution for industrial small-defect detection.