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A Grain Boundary Defects Detection Algorithm with Improved Localization Accuracy Based on EfficientDet

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Abstract

Defects detection is one of the most important tasks in the materials industry. The existence of grain boundary defects causes the crystal structure to be susceptible to corrosion, which leads to a significant reduction in metal plasticity, hardness, and tensile strength. At present, some deep learning methods have been proposed to detect such problems based on HRTEM (high-resolution transmission electron microscope) images of crystal defects. However, they face the problem of low detection rate and low localization accuracy. In this paper, an improved detection algorithm has been proposed. Firstly, to balance the performance and complexity, the EfficientDet based network is adopted in the algorithm. Secondly, a weighted fusion module is introduced to the EfficientDet network to integrate the output of features from the backbone and BiFPN (bidirectional feature pyramid network) to achieve good detection accuracy. Finally, the location loss function of the network is substituted by \(CIoU\) (complete intersection over union) loss, which can improve the defects localization accuracy. The experimental results show that compared to the initial algorithms, the AP (average precision) value of grain boundary defect detection can be improved by about 5%.

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Funding

This work is supported by MOE Planned Project of Humanities and Social Sciences (no. 20YJA870014).

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Authors and Affiliations

  1. North China University of Technology, 100144, Beijing, China

    Fuqi Mao, Jing Li, Jian Yang, Zhi Liu & Mengmeng Zhang

  2. China University of Geosciences (Beijing), 100083, Beijing, China

    Fuqi Mao

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  1. Fuqi Mao
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  2. Jing Li
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  3. Jian Yang
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  4. Zhi Liu
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Correspondence to Zhi Liu or Mengmeng Zhang.

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Fuqi Mao, Li, J., Yang, J. et al. A Grain Boundary Defects Detection Algorithm with Improved Localization Accuracy Based on EfficientDet. Aut. Control Comp. Sci. 57, 81–92 (2023). https://doi.org/10.3103/S0146411623010078

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