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Detection of Black Holes01:10

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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
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Updated: Sep 22, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data.

Quoc-Bao Ta1, Thanh-Canh Huynh2,3, Quang-Quang Pham1

  • 1Department of Ocean Engineering, Pukyong National University, Busan 48513, Korea.

Sensors (Basel, Switzerland)
|May 20, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning method using synthesized data to detect corroded bolts in steel structures. The approach accurately identifies various corrosion levels, improving structural integrity monitoring.

Keywords:
Mask-RCNNbolt corrosionbolted connectiondeep learningimage processingsteel structuresvision-based approach

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Area of Science:

  • Structural Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Deep learning models require extensive datasets for optimal performance.
  • Limited datasets hinder accuracy and generalization in neural networks.
  • Existing corroded bolt detection methods struggle with partially rusted components.

Purpose of the Study:

  • To develop a robust deep learning method for identifying corroded bolts in steel structures.
  • To address the limitations of existing methods in detecting partially corroded bolts.
  • To improve the accuracy and generalization of corrosion detection using synthesized data.

Main Methods:

  • Utilized a Mask Region-based Convolutional Neural Network (Mask-RCNN) with a Resnet50 backbone and Feature Pyramid Network.
  • Developed a four-step procedure for autonomously synthesizing diverse training datasets of corroded bolts.
  • Trained the Mask-RCNN detector on the synthesized datasets to identify corrosion severity.

Main Results:

  • The proposed method achieved high accuracy in detecting corroded bolts across varying distances and perspectives.
  • Achieved an overall accuracy rate of 96.3% at a 1.0 m capturing distance.
  • Demonstrated 97.5% accuracy for a 15° perspective angle, confirming robustness.

Conclusions:

  • The deep learning approach effectively detects corroded bolts and classifies their severity using synthesized data.
  • The method offers a reliable solution for monitoring the structural integrity of steel components.
  • Synthesized data generation is a viable strategy to enhance deep learning model performance in corrosion detection.