Steel surface defect detection and localization based on SVD and two-side compressive measurements

This paper proposes a method for defect detection and localization based on singular value decomposition and two-side compressive measurements. First, the feasibility of the singular value decomposition for defect detection and localization is analyzed, then the invariance of the geometrical structure of the rows or columns of the raw data and the compressive data is justified, so the energy and pattern contained in the raw data can be transferred into the compressive data and kept in the singular values and singular vectors. On this basis, the proposed defect detection algorithm based on the singular values of compressive data and the proposed defect localization algorithm based on the singular vectors are given without reconstruction of images. Simulation results show that the proposed method based on compressive measurements has a good performance.