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ORIGINAL RESEARCH article

Front. Phys.
Sec. Radiation Detectors and Imaging
Volume 12 - 2024 | doi: 10.3389/fphy.2024.1451165
This article is part of the Research Topic Advanced Deep Learning Algorithms for Multi-Source Data and Imaging View all 6 articles

Multiclass Small Target Detection Algorithm for Surface Defects of Chemicals Special Steel

Provisionally accepted
Shaofeng Yan Shaofeng Yan Yuanyuan Wang Yuanyuan Wang *Hauwa S. Abdullahi Hauwa S. Abdullahi Shangbing Gao Shangbing Gao *Haiyan Zhang Haiyan Zhang *Hu Zhao Hu Zhao *Xiuchuan Chen Xiuchuan Chen *
  • School of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China

The final, formatted version of the article will be published soon.

    Chemical special steels are widely used in chemical equipment manufacturing and other fields, and small defects on its surface (such as cracks and punches) are easy to cause serious accidents in harsh environments. In order to solve this problem, this paper proposes an improved defect detection algorithm for chemical special steel based on YOLOv8. Firstly, in order to effectively capture local and global information, a ParC2Net (Parallel-C2f) structure is proposed for feature extraction, which can accurately capture the subtle features of steel defects. Secondly, the loss function is adjusted to MPD-IOU, and its dynamic non-monotonic focusing characteristics are used to effectively solve the overfitting problem of the bounding box of low-quality targets. In addition, RepGFPN is used to fuse multi-scale features, deepen the interaction between semantics and spatial information, and significantly improve the efficiency of cross-layer information transmission. Finally, the RexSE-Head (ResNeXt-Squeeze-Excitation) design is adopted to enhance the positioning accuracy of small defect targets. The experimental results show that the mAP@0.5 of the improved model reaches 93.5%, and the number of parameters is only 3.29M, which realizes the high precision and high response performance of the detection of small defects in chemical special steels, and highlights the practical application value of the model. The code is available at https://github.com/improvment/prsyolo。

    Keywords: Object detection algorithms1, Steel defects 2, YOLOV83, ParC2Net4, Small targets5

    Received: 18 Jun 2024; Accepted: 12 Aug 2024.

    Copyright: © 2024 Yan, Wang, Abdullahi, Gao, Zhang, Zhao and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yuanyuan Wang, School of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China
    Shangbing Gao, School of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China
    Haiyan Zhang, School of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China
    Hu Zhao, School of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China
    Xiuchuan Chen, School of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.