Automatic detection of welding defects using the convolutional neural network

Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects

Craquelure as a Graph: Application of Image Processing and Graph Neural Networks to the Description of Fracture Patterns

Cracks on a painting is not a defect but an inimitable signature of an artwork which can be used for origin examination, aging monitoring, damage identification, and even forgery detection. This work presents the development of a new methodology and corresponding toolbox for the extraction and characterization of information from an image of a craquelure pattern. The proposed approach processes craquelure network as a graph. The graph representation captures the network structure via mutual organization of junctions and fractures. Furthermore, it is invariant to any geometrical distortions. At the same time, our tool extracts the properties of each node and edge individually, which allows to characterize the pattern statistically. We illustrate benefits from the graph representation and statistical features individually using novel Graph Neural Network and hand-crafted descriptors correspondingly. However, we also show that the best performance is achieved when both techniques are merged into one framework. We perform experiments on the dataset for paintings' origin classification and demonstrate that our approach outperforms existing techniques by a large margin.Comment: Published in ICCV 2019 Workshop

Automated Defect Detection and Characterization on Pulse Thermography Images Using Computer Vision Techniques

Defect detection and characterization plays a vital role in predicting the life span of materials. Defect detection using appropriate inspection technologies at various phases has gained huge importance in metal production lines. It can be accomplished through wise application of non-destructive testing and evaluation (NDE). It is important to characterize defects at an early stage in order to be able to overcome them or take corrective measures. Pulse thermography is a modern NDE method that can be used for defect detection in metal objects. Only a limited amount of work has been done on automated detection and characterization of defects due to thermal diffusion. This paper proposes a system for automatic defect detection and characterization in metal objects using pulse thermography images as well as various image processing algorithms and mathematical tools. An experiment was carried out using a sequence of 250 pulse thermography images of an AISI 316 L stainless steel sheet with synthetic defects. The proposed system was able to detect and characterize defects sized 10 mm, 8 mm, 6 mm, 4 mm and 2 mm with an average accuracy of 96%, 95%, 84%, 77%, 54% respectively. The proposed technique helps in the effective and efficient characterization of defects in metal objects

Design of Mobile Application for Assisting Color Blind People to Identify Information on Sign Boards

Color blindness is a condition where a person cannot distinguish colors that are of similar contrast. This paper reports an attempt to develop a mobile phone application that can run on any Android or Windows smart phone. The developed application/software tool is able to assist color blind people by converting an image with low contrast to an image with high contrast. The objective of the proposed work was to develop a program on the LabVIEW platform to i) acquire the image whose information should be processed, ii) develop an algorithm to display a high-contrast crisp image of the actual dull image, and iii) identify the colors and characters present in the dull image for messaging to the user's phone. The work was implemented on the LabVIEW platform making use of various image processing tools to identify the color and text from the sign board that otherwise cannot be identified by color blind persons. The implementation was tested with several inputs to validate the performance of the proposed method. It was able to produce accurate results for more than 97.3% of the test inputs

3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries

Recent advances in electron microscopy have enabled the imaging of single cells in 3D at nanometer length scale resolutions. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. Enabling such simulations requires watertight meshing of electron micrograph images into 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the Finite Element Method. In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. We demonstrate the application of a workflow using GAMer 2 to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and show that the resulting meshes are suitable for finite element simulations. This work is an important step towards making physical simulations of biological processes in realistic geometries routine. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery at the interface of geometry and cellular processes. We posit that a new frontier at the intersection of computational technologies and single cell biology is now open.Comment: 39 pages, 14 figures. High resolution figures and supplemental movies available upon reques

Allosteric modulation of beta1 integrin function induces lung repair in animal model of emphysema.

Emphysema is a progressive lung disease characterised by loss of lung parenchyma with associated functional changes including decreased tissue elastance. Here we report beta1 integrin is a novel target for tissue repair and regeneration in emphysema. We show a single dose of a monoclonal antibody against beta1 integrin induced both functional and structural reversal of elastase-induced lung injury in vivo, and we found that similar matrix remodelling changes occurred in human lung tissue. We also identified a potential mechanism of action as this allosteric modulation of beta1 integrin inhibited elastase-induced caspase activation, F-actin aggregate formation and changes in cellular ATP levels. This was accompanied by maintenance of beta1?integrin levels and inhibition of caveolin-1 phosphorylation. We propose that allosteric modulation of beta1 integrin-mediated mechanosensing prevents cell death associated with lung injury and progressive emphysema, thus allowing cells to survive and for repair and regeneration to ensue