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Updated: Aug 8, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Fast Attention CNN for Fine-Grained Crack Segmentation.

Hyunnam Lee1, Juhan Yoo2

  • 1Incheon International Airport Corporation, Incheon 22382, Republic of Korea.

Sensors (Basel, Switzerland)
|February 28, 2023
PubMed
Summary
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Graph Model-Based Lane-Marking Feature Extraction for Lane Detection.

Sensors (Basel, Switzerland)ยท2021
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This study introduces a fast deep learning model for precise crack detection in images. The novel network effectively identifies fine cracks and reduces noise, offering improved accuracy with significantly lower computational cost.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Materials Science

Background:

  • Deep learning excels at image segmentation for pixel-level crack detection.
  • Accurate detection of fine-grained cracks and elimination of crack-like noise are critical challenges.
  • Existing methods struggle with subtle cracks and noise like grooving.

Purpose of the Study:

  • To develop a fast encoder-decoder network with scaling attention for enhanced crack detection.
  • To improve the accuracy of detecting tiny cracks and suppress irrelevant crack-like noise.
  • To reduce computational complexity while maintaining high detection performance.

Main Methods:

  • Proposed a fast encoder-decoder network incorporating an Atrous Spatial Pyramid Pooling (ASPP) layer.
Keywords:
convolutional neural networkcrack detectionimage segmentationsalient object detection

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  • Introduced a novel scaling attention mechanism (AG+) to suppress non-semantic regions and noise.
  • Generated a comprehensive crack dataset with 11,226 image-mask pairs for training and evaluation.

    • Main Results:

    • The model demonstrated improved detection accuracy for fine-grained cracks.
    • The novel AG+ attention mechanism effectively reduced crack-like noise.
    • Achieved performance close to state-of-the-art models with a mean Dice coefficient (mDice) difference of only 1.2%.
    • Reduced computational complexity by achieving two times fewer FLOPs (floating-point operations).

    Conclusions:

    • The proposed deep learning model offers a computationally efficient and accurate solution for pixel-level crack detection.
    • The integration of ASPP and scaling attention (AG+) significantly enhances the ability to detect fine cracks and filter noise.
    • This approach provides a promising advancement for structural health monitoring and material defect analysis.