Metal surface defect segmentation can play an important role in dealing with the issue of quality control during the production and manufacturing stages. There are still two major challenges in industrial applications. One is the case that the number of metal surface defect samples is severely insufficient, and the other is that the most existing algorithms can only be used for specific surface defects and it is difficult to generalize to other metal surfaces. In this work, a theory of few-shot metal generic surface defect segmentation is introduced to solve these challenges. Simultaneously, the Triplet-Graph Reasoning Network (TGRNet) and a novel dataset Surface Defects- 4 ⁱ are proposed to achieve this theory. In our TGRNet, the surface defect triplet (including triplet encoder and trip loss) is proposed and is used to segment background and defect area, respectively. Through triplet, the few-shot metal surface defect segmentation problem is transformed into few-shot semantic segmentation problem of defect area and background area. For few-shot semantic segmentation, we propose a method of multi-graph reasoning to explore the similarity relationship between different images. And to improve segmentation performance in the industrial scene, an adaptive auxiliary prediction module is proposed. For Surface Defects- 4 ⁱ , it includes multiple categories of metal surface defect images to verify the generalization performance of our TGRNet and adds the nonmetal categories (leather and tile) as extensions. Through extensive comparative experiments and ablation experiments, it is proved that our architecture can achieve state-of-the-art results.

Nondestructive testing of welding images is still a significant challenge due to the imaging characteristics of radiographic images and the extremely random distribution of welding defect types. The practical application of supervised methods for welding nondestructive testing encounters significant challenges due to the limited availability of densely annotated samples and the absence of prior information regarding unknown defects. Hence, this article first proposes a rapid screening method using only defect-free image training to screen suspected defect images and normal images of welds in real time. Specifically, we first combine the correspondence mechanism and the representation mechanism, aiming to: 1) alleviate the smoothing reconstruction behavior caused by small defects and weak texture defects in weld; and 2) mitigate the offset learning behavior resulting from the differences between natural images and industrial weld images in the normalizing flow method. We propose a memory-aware transformer-based encoder, thus improving representations of complex defect-free images. Moreover, a dual-decoder strategy is introduced, which remaps the latent dependencies generated by the encoder through a semantic correspondence mechanism and reconstruction-guided normalizing flow, enabling effective learning of knowledge from weld images. We apply this framework to an industrial case of weld images, the experimental results demonstrate that our method outperforms other existing approaches.

Grain size plays a fundamental role in the mechanical properties of materials. Recently, automatic measurement of average grain size attacks more and more attention based on computer vision. However, low contrast, twin grains, thin boundary, and low connectivity limit the achievement of automatic and accurate image analysis. Inspired by the calculation procedure of grain size, we propose a center-guided and connectivity-preserving network for grain boundaries segmentation. On the one hand, the proposed center feature extraction module and center-guided feature recalibration mechanism make the network pay more attention to the center area. On the other hand, a connectivity-preserving loss function is integrated with the network, which forces the network to converge towards high connectivity. Benefit by above aspects, our network can segment the grain boundary with high structural integrity and avoid the complex post-processing process. Experiments on the SRIF-GSM dataset reveal that our method achieves 85.98 mIoU and 95.60 clDice scores, demonstrating significant advantages compared with the state-of-the-art semantic segmentation methods.

This paper explores the block-based singular-value-decomposition (SVD) with applications to image compression and processing in both static and dynamic cases. Results include a comprehensive performance study of rank-1 asymmetric block sizes in comparison to conventional uniform square blocks for static image compression. In addition, a method of movement detection for video streaming applications are presented and discussed in this paper. We show that by tracking the Frobenius norm of the derivatives of sequential images, represented as a time series matrix, allows one to infer change points in varying lighting conditions and foreground movement in video sequences. We show that by combining SVD background subtraction we can create clearer distinction between movements and changes in lighting intensity. It is also demonstrated that one can maintain the original derivative feature after re-scaling the images of the video stream to a fraction of their original resolution via local averaging.