Smart-Inspect: Micro Scale Localization and Classification of Smartphone Glass Defects for Industrial Automation

• URL: http://arxiv.org/abs/2010.00741v1
• Date: Fri, 2 Oct 2020 01:15:00 GMT
• Title: Smart-Inspect: Micro Scale Localization and Classification of Smartphone Glass Defects for Industrial Automation
• Authors: M Usman Maqbool Bhutta, Shoaib Aslam, Peng Yun, Jianhao Jiao and Ming Liu
• Abstract summary: We present a robust semi-supervised learning framework for intelligent micro-scaled localization and classification of defects on a 16K pixel image of smartphone glass. Our model features the efficient recognition and labeling of three types of defects: scratches, light leakage due to cracks, and pits. Our method also differentiates between the defects and light reflections due to dust particles and sensor regions, which are classified as non-defect areas.
• Score: 8.414611978466622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The presence of any type of defect on the glass screen of smart devices has a great impact on their quality. We present a robust semi-supervised learning framework for intelligent micro-scaled localization and classification of defects on a 16K pixel image of smartphone glass. Our model features the efficient recognition and labeling of three types of defects: scratches, light leakage due to cracks, and pits. Our method also differentiates between the defects and light reflections due to dust particles and sensor regions, which are classified as non-defect areas. We use a partially labeled dataset to achieve high robustness and excellent classification of defect and non-defect areas as compared to principal components analysis (PCA), multi-resolution and information-fusion-based algorithms. In addition, we incorporated two classifiers at different stages of our inspection framework for labeling and refining the unlabeled defects. We successfully enhanced the inspection depth-limit up to 5 microns. The experimental results show that our method outperforms manual inspection in testing the quality of glass screen samples by identifying defects on samples that have been marked as good by human inspection.

Related papers

• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• 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)
• Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics [27.03052142039447]
  We introduce the Defect Spectrum, a comprehensive benchmark that offers precise, semantic-abundant, and large-scale annotations for a wide range of industrial defects. Building on four key industrial benchmarks, our dataset refines existing annotations and introduces rich semantic details, distinguishing multiple defect types within a single image. We also introduce Defect-Gen, a two-stage diffusion-based generator designed to create high-quality and diverse defective images.
  arXiv  Detail & Related papers  (2023-10-26T11:23:24Z)
• 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)
• Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug [53.682955739083056]
  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
  arXiv  Detail & Related papers  (2023-01-19T11:37:20Z)
• Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach [2.7180863515048674]
  We have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision. We are aiming at detecting and segmenting different types of inter-class defect patterns such as bridge, break, and line collapse.
  arXiv  Detail & Related papers  (2022-11-03T23:26:40Z)
• 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)
• 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)
• Optical Inspection of the Silicon Micro-strip Sensors for the CBM Experiment employing Artificial Intelligence [0.0]
  In this manuscript, we present the analysis of various sensor surface defects. Defect detection was done using the application of Convolutional Deep Neural Networks (CDNNs) Based on the total number of defects found on the sensor's surface, a method for the estimation of sensor's overall quality grade and quality score was proposed.
  arXiv  Detail & Related papers  (2021-07-16T05:48:22Z)
• Cognitive Visual Inspection Service for LCD Manufacturing Industry [80.63336968475889]
  This paper discloses a novel visual inspection system for liquid crystal display (LCD), which is currently a dominant type in the FPD industry. System is based on two cornerstones: robust/high-performance defect recognition model and cognitive visual inspection service architecture.
  arXiv  Detail & Related papers  (2021-01-11T08:14:35Z)
• 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)

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.