当前位置: X-MOL 学术Autom. Constr. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Hybrid-Segmentor: Hybrid approach for automated fine-grained crack segmentation in civil infrastructure
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-07 , DOI: 10.1016/j.autcon.2024.105960
June Moh Goo, Xenios Milidonis, Alessandro Artusi, Jan Boehm, Carlo Ciliberto

It is essential to detect and segment cracks in various infrastructures, such as roads and buildings, to ensure safety, longevity, and cost-effective maintenance. Despite deep learning advancements, precise crack detection across diverse conditions remains challenging. This paper introduces Hybrid-Segmentor, a deep learning model combining Convolutional Neural Networks-based and Transformer-based architectures to extract both fine-grained local features and global crack patterns, significantly enhancing crack detection for improved infrastructure maintenance. Hybrid-Segmentor, trained on a large custom dataset created by merging multiple open-source datasets, can accurately detect cracks on different types of surfaces, crack shapes, and sizes. The model demonstrates robustness and versatility by accurately detecting discontinuities, vague cracks, non-crack regions within crack areas, blurred images, and complex crack contours. Furthermore, when compared against other recent models for crack segmentation, the proposed model achieves state-of-the-art performance, significantly outperforming them across five key metrics: accuracy (0.971), precision (0.807), recall (0.756), F1-score (0.774), and IoU (0.631).

中文翻译:


Hybrid-Segmentor:在土木基础设施中实现自动细粒度裂缝分割的混合方法



检测和分割各种基础设施(如道路和建筑物)中的裂缝至关重要,以确保安全、使用寿命和成本效益。尽管深度学习取得了进步,但在不同条件下进行精确裂纹检测仍然具有挑战性。本文介绍了 Hybrid-Segmentor,这是一种深度学习模型,结合了基于卷积神经网络和基于 Transformer 的架构,可提取细粒度的局部特征和全局裂纹模式,从而显著增强裂纹检测,从而改进基础设施维护。Hybrid-Segmentor 在通过合并多个开源数据集创建的大型自定义数据集上进行训练,可以准确检测不同类型表面、裂缝形状和大小的裂缝。该模型通过准确检测不连续性、模糊裂纹、裂纹区域内的非裂纹区域、模糊图像和复杂的裂纹轮廓,展示了稳健性和多功能性。此外,与其他最近的裂纹分割模型相比,所提出的模型实现了最先进的性能,在五个关键指标上明显优于它们:准确率 (0.971)、精确率 (0.807)、召回率 (0.756)、F1 分数 (0.774) 和 IoU (0.631)。
更新日期:2025-01-07
down
wechat
bug