Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

The present study was aimed at discovering novel biologically active compounds based on the skeletons of gingerol and shogaol, the pungent principles from the rhizomes of Zingiber officinale. Therefore, eight groups of analogues were synthesized and examined for their inhibitory activities of platelet aggregation induced by arachidonic acid, collagen, platelet activating factor, and thrombin. Among the tested compounds, [6]-paradol (5b) exhibited the most significant anti-platelet aggregation activity. It was the most potent candidate, which could be used in further investigation to explore new drug leads.

Generic object detection algorithms for natural images have been proven to have excellent performance. In this paper, fabric defect detection on optical image datasets is systematically studied. In contrast to generic datasets, defect images are multi-scale, noise-filled, and blurred. Back-light intensity would also be sensitive for visual perception. Large-scale fabric defect datasets are collected, selected, and employed to fulfill the requirements of detection in industrial practice in order to address these imbalanced issues. An improved two-stage defect detector is constructed for achieving better generalization. Stacked feature pyramid networks are set up to aggregate cross-scale defect patterns on interpolating mixed depth-wise block in stage one. By sharing feature maps, center-ness and shape branches merges cascaded modules with deformable convolution to filter and refine the proposed guided anchors. After balanced sampling, the proposals are down-sampled by position-sensitive pooling for region of interest, in order to characterize interactions among fabric defect images in stage two. The experiments show that the end-to-end architecture improves the occluded defect performance of region-based object detectors as compared with the current detectors.

Figure 1: Given input portrait images with varying poses and expressions (bottom), our approach can automatically generate shapely portraits (top) that are better proportioned, by estimating the best reshaping parameter setting (called shapely degree) using deep learning.

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An efficient, TFA-promoted cascade annulation of sulfur ylides, 2-hydroxy-β-nitrostyrenes and indoles was established.

Guanine deaminase (GDA) deaminates guanine to xanthine. Despite its significance, the study of human GDA remains limited compared to other metabolic deaminases. As a result, its substrate and inhibitor repertoire are limited, and effective real-time activity, inhibitory, and discovery assays are missing. Herein, we explore two emissive heterocyclic cores, based on thieno­[3,4-d]­pyrimidine ( thN) and isothiazole­[4,3-d]­pyrimidine ( tzN), as surrogate GDA substrates. We demonstrate that, unlike the thieno analog, thG N , the isothiazolo guanine surrogate, tzG N , does undergo effective enzymatic deamination by GDA and yields the spectroscopically distinct xanthine analog, tzX N . Further, we showcase the potential of this fluorescent nucleobase surrogate to provide a visible spectral window for a real-time study of GDA and its inhibition.