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.

This paper proposes a regularized blind deconvolution method for restoring Poissonian blurred image. The problem is formulated by utilizing the L0-norm of image gradients and total variation (TV) to regularize the latent image and point spread function (PSF), respectively, and combining them with the negative logarithmic Poisson log-likelihood. To solve the problem, we propose an approach which combines the methods of variable splitting and Lagrange multiplier to convert the original problem into three sub-problems, and then design an alternating minimization algorithm which incorporates the estimation of PSF and latent image as well as the updation of Lagrange multiplier into account. We also design a non-blind deconvolution method based on TV regularization to further improve the quality of the restored image. Experimental results on both synthetic and real-world Poissonian blurred images show that the proposed method can achieve restored images of very high quality, which is competitive with or even better than some state of the art methods.

Most existing line segment detection methods suffer from the over-segmentation phenomenon. An improved line segment detection method is presented in this work, which can generate more and longer line segments, yet still accurately reflect the structural details of the image. Line segment grouping, line segment validation and a multiscale framework are adopted to reach this end. Specifically, smart grouping rules are introduced to locate potential homologous line segments (derived from the same boundaries). Novel merging criteria based on Helmholtz principle is then used to evaluate the meaningfulness between separate line segments and their merged ones. The improved multiscale framework facilitates line segments merging in detection and post-detection processes, yielding more high-quality line segments. Finally, the proposed method is compared with four leading methods on the famous public dataset, YorkUrban-LineSegment. Experimental results demonstrate that the method has good continuity and outperforms the leading methods in F-measure.

Surface defect detection is extremely critical for quality control and routine maintenance, and computer vision-based methods have delivered promising performance in various industrial fields. However, there remains big challenges in region consistency and boundary localization, due to complicated appearance in defects, interference of artifacts, and low contrast. To overcome these issues, in this paper, we propose an enhanced encoder-decoder network with hierarchical supervision for surface defect detection, termed SDDet. Specifically, deformable convolution is adopted to enable feature learning in a free-form way, thus making the network more robust to various defect structures. Then a bridge module is applied to capture richer multi-level contextual information, which is conducive to distinguishing defects from the background artifacts. Next, the learned features from adjacent layers are adaptively combined by selective feature aggregation module to enhance discriminative features and suppress valueless features. Meanwhile, the bottleneck refinement module is added to further refine the boundary of defect region, resulting in a satisfying boundary details. Finally, a hybrid three-level loss is developed to guide the network to focus more on error-prone hard pixels. Additionally, a hardware system embedded with our SDDet is built to achieve automatic and accurate defect inspection. Experimental results over four datasets demonstrate the effectiveness and generalization of our method compared with state-of-the-art methods.