Defect detection and segmentation in X-Ray images of magnesium alloy castings using the Detectron2 framework

• URL: http://arxiv.org/abs/2202.13945v1
• Date: Mon, 28 Feb 2022 16:53:09 GMT
• Title: Defect detection and segmentation in X-Ray images of magnesium alloy castings using the Detectron2 framework
• Authors: Francisco Javier Yag\"ue, Jose Francisco Diez-Pastor, Pedro Latorre-Carmona, Cesar Ignacio Garcia Osorio
• Abstract summary: New production techniques have made it possible to produce metal parts with more complex shapes, making the quality control process more difficult. X-Ray images has made this process much easier, allowing not only to detect superficial defects in a much simpler way, but also to detect welding or casting defects. The aim of this paper is to apply a deep learning system based on Detectron2, a state-of-the-art library applied to object detection and segmentation in images.
• Score: 0.13764085113103217
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: New production techniques have emerged that have made it possible to produce metal parts with more complex shapes, making the quality control process more difficult. This implies that the visual and superficial analysis has become even more inefficient. On top of that, it is also not possible to detect internal defects that these parts could have. The use of X-Ray images has made this process much easier, allowing not only to detect superficial defects in a much simpler way, but also to detect welding or casting defects that could represent a serious hazard for the physical integrity of the metal parts. On the other hand, the use of an automatic segmentation approach for detecting defects would help diminish the dependence of defect detection on the subjectivity of the factory operators and their time dependence variability. The aim of this paper is to apply a deep learning system based on Detectron2, a state-of-the-art library applied to object detection and segmentation in images, for the identification and segmentation of these defects on X-Ray images obtained mainly from automotive parts

Related papers

• Capability enhancement of the X-ray micro-tomography system via ML-assisted approaches [0.8999666725996978]
  Ring artifacts in X-ray micro-CT images are one of the primary causes of concern in their accurate visual interpretation and quantitative analysis. This article presents a convolution neural network (CNN)-based Deep Learning (DL) model inspired by UNet with a series of encoder and decoder units with skip connections for removal of ring artifacts.
  arXiv  Detail & Related papers  (2024-02-08T14:23:24Z)
• Segment Anything in Defect Detection [38.85728242930962]
  DefectSAM is a novel approach for segmenting defects on highly noisy thermal images. It surpasses existing state-of-the-art segmentation algorithms and achieves significant improvements in defect detection rates.
  arXiv  Detail & Related papers  (2023-11-17T00:28:19Z)
• Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs [59.19218582436495]
  We propose an unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED. Skin lesions are segmented by minimizing the structural entropy of a superpixel graph constructed from the dermoscopic image. We characterize the consistency of healthy skin features and devise a novel multi-scale segmentation mechanism by outlier detection, which enhances the segmentation accuracy by leveraging the superpixel features from multiple scales.
  arXiv  Detail & Related papers  (2023-09-05T02:15:51Z)
• Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review [4.493547775253646]
  Machine learning algorithms can be trained to accurately classify and locate defects in semiconductor samples. Convolutional neural networks have proved to be particularly useful in this regard. This systematic review provides a comprehensive overview of the state of automated semiconductor defect inspection on SEM images.
  arXiv  Detail & Related papers  (2023-08-16T13:59:43Z)
• X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics [55.6985304397137]
  We propose an uncertainty-aware 3D reconstruction model that translates X-ray images to EM-like images with enhanced membrane segmentation quality. This shows its potential for developing simpler, faster, and more accurate X-ray based connectomics pipelines.
  arXiv  Detail & Related papers  (2023-03-02T00:52:41Z)
• Recognition of Defective Mineral Wool Using Pruned ResNet Models [88.24021148516319]
  We developed a visual quality control system for mineral wool. X-ray images of wool specimens were collected to create a training set of defective and non-defective samples. We obtained a model with more than 98% accuracy, which in comparison to the current procedure used at the company, it can recognize 20% more defective products.
  arXiv  Detail & Related papers  (2022-11-01T13:58:02Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Reference-based Defect Detection Network [57.89399576743665]
  The first issue is the texture shift which means a trained defect detector model will be easily affected by unseen texture. The second issue is partial visual confusion which indicates that a partial defect box is visually similar with a complete box. We propose a Reference-based Defect Detection Network (RDDN) to tackle these two problems.
  arXiv  Detail & Related papers  (2021-08-10T05:44:23Z)
• Deep Learning Based Defect Detection for Solder Joints on Industrial X-Ray Circuit Board Images [14.139826850432339]
  In this paper, deep learning is incorporated in X-ray imaging based quality control during PCB quality inspection. Two artificial intelligence (AI) based models are proposed and compared for joint defect detection. The efficacy of the proposed methods are verified through experimenting on a real-world 3D X-ray dataset.
  arXiv  Detail & Related papers  (2020-08-06T12:25:18Z)
• Learning-based Defect Recognition for Quasi-Periodic Microscope Images [0.0]
  We propose a semi-supervised machine learning method that assists in the detection of lattice defects from atomic resolution microscope images. It involves a convolutional neural network that classifies image patches as defective or non-defective, a graph-based that chooses one non-defective patch as a model, and finally an automatically generated convolutional filter bank. The algorithm is tested on III-V/Si crystalline materials and successfully evaluated against different metrics, showing promising results even for extremely small training data sets.
  arXiv  Detail & Related papers  (2020-07-02T18:00:02Z)
• Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module [50.75589128518707]
  We contribute the first high-quality object detection dataset for security inspection, named OPIXray. OPIXray focused on the widely-occurred prohibited item "cutter", annotated manually by professional inspectors from the international airport. We propose the De-occlusion Attention Module (DOAM), a plug-and-play module that can be easily inserted into and thus promote most popular detectors.
  arXiv  Detail & Related papers  (2020-04-18T16:10:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.