Surface Defects Detection Using Non-convex Total Variation Regularized RPCA With Kernelization

Surface defects have an adverse effect on the quality of industrial products, and vision-based defect detection is widely researched due to its objective and stable performance. However, the task is still challenging due to diversified defect types and complex background texture. The robust principal component analysis (RPCA) has proven applicable in defect inspection by regarding nondefective background as the low-rank part and defective area as the sparse part. However, such methods cannot sufficiently detect defects due to complex cluttered background, noise interference, and limited features available. To address these issues, in this article, we proposed an unsupervised surface defect detection method based on nonconvex total variation (TV) regularized RPCA with kernelization, named KRPCA-NTV. Specifically, the kernel method is integrated into RPCA to better handle complex cluttered background lying in a nonstrict low-rank subspace. Furthermore, nonconvex TV regularization is introduced to prevent the noise pixel from being separated into the defect region; meanwhile, nonconvex optimization promotes higher solution accuracy. In addition, the kernel canonical correlation analysis (KCCA) is utilized to fuse complementary features for boosting feature representation ability. To demonstrate the superiority and robustness of the proposed method, we compare it with the state of the art on five defect data sets; the results show that the proposed method outperforms competing methods in terms of accuracy and generalizability.