Research from Lebanese International University Provides New Data on Support Vec tor Machines (Learning-Based Approach for Automated Surface Inspection with Indu strial Tomography Imaging)
Research from Lebanese International University Provides New Data on Support Vec tor Machines (Learning-Based Approach for Automated Surface Inspection with Indu strial Tomography Imaging)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on support vector machines is now available. According to news originating from Lebanese International Uni versity by NewsRx editors, the research stated, “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 en hanced inspection accuracy, all while significantly reducing costs in the manufa cturing industry.” The news correspondents obtained a quote from the research from Lebanese Interna tional University: “However, the lack of actual steel surface defects datasets c urrently places a certain constraint on further research into classifying those anomalies. As a consequence, the Convolutional Neural Network (CNN) technique, k nown for its prowess in image-related tasks, faces certain challenges, especiall y in classifying less common defects. This work proposes a novel hybrid CNN mode l with a Support Vector Machine (SVM) classifier at the output layer for surface defects classification. The features extracted from the pre-trained ResNet152 a nd EfficientB0 CNN algorithms are concatenated and fed to the SVM layer for clas sification. Extensive experiments on a merged dataset consisting of the publicly available Northeastern University (NEU) dataset and Xsteel surface defect datas et (X-SDD) are carried out and the accuracy and F1 scores are calculated for per formance evaluation. The merged dataset contains eleven typical defect types wit h a total of 2660 defect images.”
Key words
Lebanese International University/Machi ne Learning/Support Vector Machines
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