Learning-Based Approach for Automated Surface Inspection with Industrial Tomography Imaging

In recent years, advanced deep learning techniques have emerged as pivotal tools in enabling the development of robust vision-based solutions for steel surface inspection. This resulted in enhanced inspection accuracy, all while significantly reducing costs in the manufacturing industry. However, the lack of actual steel surface defects datasets currently places a certain constraint on further research into classifying those anomalies. As a consequence, the Convolutional Neural Network (CNN) technique, known for its prowess in image-related tasks, faces certain challenges, especially in classifying less common defects. This work proposes a novel hybrid CNN model with a Support Vector Machine (SVM) classifier at the output layer for surface defects classification. The features extracted from the pre-trained ResNet152 and EfficientB0 CNN algorithms are concatenated and fed to the SVM layer for classification. Extensive experiments on a merged dataset consisting of the publicly available Northeastern University (NEU) dataset and Xsteel surface defect dataset (X-SDD) are carried out and the accuracy and F1 scores are calculated for performance evaluation. The merged dataset contains eleven typical defect types with a total of 2660 defect images. Then, the adopted algorithm is compared with ten fine-tuned deep learning models to evaluate the performance of transfer learning for steel defect detection and identification. The evaluation results show that the deep feature extraction and SVM classification produced better results than the transfer learning. Finally, the proposed classifier model is validated on a newly collected dataset from a Computed Tomography scanner with an accuracy reaching over 96%.