Automated Defect Recognition of Castings defects using Neural Networks

• URL: http://arxiv.org/abs/2209.02279v1
• Date: Tue, 6 Sep 2022 08:10:48 GMT
• Title: Automated Defect Recognition of Castings defects using Neural Networks
• Authors: Alberto Garc\'ia-P\'erez, Mar\'ia Jos\'e G\'omez-Silva, Arturo de la Escalera
• Abstract summary: CNN model achieves 94.2% accuracy (mAP@IoU=50%) when applied to an automotive aluminium castings dataset (GDXray) On an industrial environment, its inference time is less than 400 ms per DICOM image, so it can be installed on production facilities with no impact on delivery time.
• Score: 2.4999739879492084
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Industrial X-ray analysis is common in aerospace, automotive or nuclear industries where structural integrity of some parts needs to be guaranteed. However, the interpretation of radiographic images is sometimes difficult and may lead to two experts disagree on defect classification. The Automated Defect Recognition (ADR) system presented herein will reduce the analysis time and will also help reducing the subjective interpretation of the defects while increasing the reliability of the human inspector. Our Convolutional Neural Network (CNN) model achieves 94.2\% accuracy (mAP@IoU=50\%), which is considered as similar to expected human performance, when applied to an automotive aluminium castings dataset (GDXray), exceeding current state of the art for this dataset. On an industrial environment, its inference time is less than 400 ms per DICOM image, so it can be installed on production facilities with no impact on delivery time. In addition, an ablation study of the main hyper-parameters to optimise model accuracy from the initial baseline result of 75\% mAP up to 94.2\% mAP, was also conducted.

Related papers

• Accelerating Domain-Aware Electron Microscopy Analysis Using Deep Learning Models with Synthetic Data and Image-Wide Confidence Scoring [0.0]
  We create a physics-based synthetic image and data generator, resulting in a machine learning model that achieves comparable precision (0.86), recall (0.63), F1 scores (0.71), and engineering property predictions (R2=0.82) Our study demonstrates that synthetic data can eliminate human reliance in ML and provides a means for domain awareness in cases where many feature detections per image are needed.
  arXiv  Detail & Related papers  (2024-08-02T20:15:15Z)
• Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Rail Defect Detection [12.584718477246382]
  This study proposes a VAE-based synthetic image generation technique for rail defects. It is applied to create a synthetic dataset for the Canadian Pacific Railway. 500 synthetic samples are generated with a minimal reconstruction loss of 0.021.
  arXiv  Detail & Related papers  (2023-12-31T04:34:58Z)
• A Computer Vision Enabled damage detection model with improved YOLOv5 based on Transformer Prediction Head [0.0]
  Current state-of-the-art deep learning (DL)-based damage detection models often lack superior feature extraction capability in complex and noisy environments. DenseSPH-YOLOv5 is a real-time DL-based high-performance damage detection model where DenseNet blocks have been integrated with the backbone. DenseSPH-YOLOv5 obtains a mean average precision (mAP) value of 85.25 %, F1-score of 81.18 %, and precision (P) value of 89.51 % outperforming current state-of-the-art models.
  arXiv  Detail & Related papers  (2023-03-07T22:53:36Z)
• Real-Time Damage Detection in Fiber Lifting Ropes Using Convolutional Neural Networks [19.093832934990044]
  We present a vision-based system for detecting damage in synthetic fiber rope images using convolutional neural networks (CNN) Experts from Konecranes annotate the collected images in accordance with the rope's condition; normal or damaged. Experimental results show the proposed model outperforms other techniques with 96.4% accuracy, 95.8% precision, 97.2% recall, 96.5% F1-score, and 99.2% AUC.
  arXiv  Detail & Related papers  (2023-02-23T11:44:43Z)
• 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)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead [67.87678914831477]
  Deep Neural Networks (DNNs) enable a wide series of technological advancements. Recent findings indicate that transient hardware faults may corrupt the models prediction dramatically. In this work, we propose to tackle the reliability issue both at training and model design time.
  arXiv  Detail & Related papers  (2022-05-28T13:09:30Z)
• Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis [0.0]
  This paper develops a more efficient variant of WasteNet, a collaborative recycling classification model. The newly developed model scores a test-set accuracy of 95.8% with a real world accuracy of 95%, a 14% increase over the original. Our acceleration pipeline boosted model throughput by 750% to 24 inferences per second on the Jetson Nano embedded device.
  arXiv  Detail & Related papers  (2021-10-21T10:42:14Z)
• TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services [77.454688257702]
  Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance. One direction aims at the recognition of re-occurring anomaly types to enable remediation automation. We propose a method that is invariant to dimensionality changes of given data.
  arXiv  Detail & Related papers  (2021-02-25T14:24:49Z)
• Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction [65.44752447868626]
  We propose collaborative boundaryaware context encoding networks called AEP-Net for error prediction task. Specifically, we propose a collaborative feature transformation branch for better feature fusion between images and masks, and precise localization of error regions. The AEP-Net achieves an average DSC of 0.8358, 0.8164 for error prediction task, and shows a high Pearson correlation coefficient of 0.9873.
  arXiv  Detail & Related papers  (2020-06-25T12:42:01Z)
• Calibrating Deep Neural Networks using Focal Loss [77.92765139898906]
  Miscalibration is a mismatch between a model's confidence and its correctness. We show that focal loss allows us to learn models that are already very well calibrated. We show that our approach achieves state-of-the-art calibration without compromising on accuracy in almost all cases.
  arXiv  Detail & Related papers  (2020-02-21T17:35:50Z)

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.