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Automatic Control and Computer Sciences

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01-02-2023

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

Authors: Fuqi Mao, Jing Li, Jian Yang, Zhi Liu, Mengmeng Zhang

Published in: Automatic Control and Computer Sciences | Issue 1/2023

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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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Title: A Grain Boundary Defects Detection Algorithm with Improved Localization Accuracy Based on EfficientDet
Authors: Fuqi Mao
Jing Li
Jian Yang
Zhi Liu
Mengmeng Zhang
Publication date: 01-02-2023
Publisher: Pleiades Publishing
Published in: Automatic Control and Computer Sciences / Issue 1/2023
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411623010078

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