WLR-Net: An Improved YOLO-V7 With Edge Constraints and Attention Mechanism for Water Leakage Recognition in the Tunnel

IF 5.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-13 DOI:10.1109/TETCI.2024.3369999

Junxin Chen;Xu Xu;Gwanggil Jeon;David Camacho;Ben-Guo He

引用次数: 0

Abstract

Water leakage recognition plays a significant role in ensuring the safety of shield tunnel lining. However, current models cannot meet the engineering requirements because the tunnel environment is complex. In this concern, a one-stage deep learning model is developed for water leakage recognition. First, we design an attention module to reduce background noise interference. Second, an edge refinement algorithm is proposed to refine the mask of water leakage region. Furthermore, a mixed data augmentation is developed to enhance the robustness of model. Experimental results indicate an average precision (AP) is up to 60%, and a recognition speed is 26 frames per second (FPS). This determines that our proposed network is lightweight and has advantages over peer methods.

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WLR-Net：带边缘约束和注意力机制的改进型 YOLO-V7 隧道漏水识别系统

漏水识别在确保盾构隧道衬砌安全方面发挥着重要作用。然而，由于隧道环境复杂，现有模型无法满足工程要求。为此，我们开发了一种用于漏水识别的单级深度学习模型。首先，我们设计了一个注意力模块，以减少背景噪声干扰。其次，我们提出了一种边缘细化算法来细化漏水区域的掩膜。此外，我们还开发了一种混合数据增强方法来提高模型的鲁棒性。实验结果表明，平均精度（AP）高达 60%，识别速度为每秒 26 帧（FPS）。这表明我们提出的网络是轻量级的，与同类方法相比具有优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Emerging Topics in Computational Intelligence Mathematics-Control and Optimization

CiteScore

10.30

自引率

7.50%

发文量

147

期刊介绍： The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys. TETCI is an electronics only publication. TETCI publishes six issues per year. Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.

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