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Image Recognition of Concrete Electromagnetic Flaw Detection Based on Improved YOLOv7

Published: 03 May 2024 Publication History

Abstract

Owing to the invisibility characteristics of the interiors of concrete structures, nondestructive testing technologies are commonly employed to detect internal damage. Electromagnetic flaw detection technology, as a prominent nondestructive testing method, offers capabilities of strong penetration and precise imaging. However, the output images produced by this detection technology are usually reliant on subjective human judgment, and existing automatic detection algorithms have limitations in terms of accuracy. To enhance the accuracy of electromagnetic flaw detection images, this paper proposes improved YOLOv7 based detection method. First of all, the ACBlock convolution module is introduced to replace the original 3×3 convolution kernel for alleviating the loss of edge feature information caused by the sampling operation of the backbone network MP module. Next, in order to have more focus on the semantic features of small targets, a global attention mechanism (GAMAttention) is incorporated into the algorithm. Then, the FReLU activation function is adopted to enhance the algorithm sensitivity to image space. Finally, experiments are conducted on a self-created concrete flaw detection image dataset to illustrate the efficiency of the proposed algorithm. The results show that the mean Average Precision (mAP) value achieved by the improved algorithm is 95.85%. which has a significant improvement of 13.89% in comparison with the pre-improved YOLOv7 algorithm and surpasses other algorithms in the same domain. These findings represent that the proposed algorithm effectively enhances the accuracy of concrete electromagnetic flaw detection image detection.

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  1. Image Recognition of Concrete Electromagnetic Flaw Detection Based on Improved YOLOv7

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        ICIGP '24: Proceedings of the 2024 7th International Conference on Image and Graphics Processing
        January 2024
        480 pages
        ISBN:9798400716720
        DOI:10.1145/3647649
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Published: 03 May 2024

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        Author Tags

        1. Attention mechanism
        2. Electromagnetic flaw detection
        3. Non-destructive testing
        4. YOLOv7

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        • Liaoning Provincial Science and Technology Department Foundation
        • Liaoning Province education department Foundation, China

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