Deep Learning-Based Automated Industrial Surface Defect Classification

Materials such as steel, concrete, and various alloys are used to build infrastructure and machinery across all industries. Due to their long service life, some of these materials will eventually develop surface damage (such as crazing, corrosion, and pitting) that will negatively affect both the structural integrity and the reliability of the machinery/infrastructure. Thus, the rapid and accurate classification of defects on material surfaces is crucial for ensuring high-quality materials and a continuous process without machinery breakdowns. In this work, we compare the effectiveness of two types of deep learning models (a VGG16 convolutional neural network with transfer learning and the state-of-the-art YOLOv8) for automatic defect classification on surfaces. The dataset used in our experiment included data from the Phase 5 Capstone Corrosion and the NEU Surface Defects Databases, resulting in eight distinct classes of surface defects. The effectiveness of both models was determined using stratified 10-fold cross-validation. The results of the experiment revealed that YOLOv8 achieved 98.5% accuracy, whereas VGG16 achieved only 92.5%. Moreover, YOLOv8 exhibited greater consistency under noise perturbations, demonstrating superior robustness compared with VGG16. Beyond model comparison, this study introduces a unified benchmark constructed from heterogeneous industrial defect datasets. It systematically evaluates classification performance, generalization capability, and robustness using stratified cross-validation and noise-based testing. The results indicate that YOLOv8 is a practical solution for automated industrial surface defect classification.