Method for detecting cracks in retaining walls based on improved YOLOv5s.

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-02-20 DOI:10.1038/s41598-025-90174-1

Yanhai Wang, Chenxin Guo, Guoyong Duan, Yuhao Zhang, Chao Yang, Huafeng Deng

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Abstract

This paper proposed an improved YOLOv5s-based method to address the challenging detection of cracks in retaining walls due to their irregular development and small size. This approach was developed by applying the YOLOv5s framework and incorporating several enhancements. Specifically, the BotNet module was introduced into the Backbone to enhance the extraction of long-term dependence and global features. The GhostNetV2 module was also utilized to reduce the network complexity, making it suitable for edge-based detection. In the Neck, the deployment of the ODConv module improved the extraction of multi-scale features. Another incorporated tool was the rotated bounding boxes for accurately localizing irregularly oriented cracks. The experimental results showed that the improved algorithm reduced the number of YOLOv5s parameters by 12% and increased the average precision by 1.5% compared to YOLOv5s using only the rotating prediction frames. In summary, the proposed algorithm outperformed the traditional YOLOv5s in better crack localization, quantity calculation, and a smaller parameter volume, presenting a suitable tool for retaining wall crack detection.

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基于改进YOLOv5s的挡土墙裂缝检测方法。

本文提出了一种基于 YOLOv5s 的改进方法，以解决挡土墙裂缝的检测难题，因为裂缝发展不规则且尺寸较小。该方法是通过应用 YOLOv5s 框架并结合几项增强功能而开发的。具体来说，在骨干网中引入了 BotNet 模块，以加强对长期依赖性和全局特征的提取。此外，还利用 GhostNetV2 模块降低了网络复杂性，使其适用于基于边缘的检测。在 Neck 中，ODConv 模块的部署改进了多尺度特征的提取。另一个工具是旋转边界框，用于精确定位不规则方向的裂缝。实验结果表明，与仅使用旋转预测框的 YOLOv5s 相比，改进后的算法减少了 12% 的 YOLOv5s 参数数量，平均精度提高了 1.5%。总之，所提出的算法在裂缝定位、数量计算和较小的参数量方面都优于传统的 YOLOv5s，是挡土墙裂缝检测的合适工具。

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

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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