Open Access

ARTICLE

Segmentation of Building Surface Cracks by Incorporating Attention Mechanism and Dilation-Wise Residual

Yating Xu¹, Mansheng Xiao^1,*, Mengxing Gao¹, Zhenzhen Liu¹, Zeyu Xiao²

1 School of Computer Science and Artificial Intelligence, Hunan University of Technology, Zhuzhou, 412007, China
2 School of Computer, Hunan University of Technology and Business, Changsha, 410000, China

* Corresponding Author: Mansheng Xiao. Email:

(This article belongs to the Special Issue: AI-driven Monitoring, Condition Assessment, and Data Analytics for Enhancing Infrastructure Resilience)

Structural Durability & Health Monitoring 2025, 19(6), 1635-1656. https://doi.org/10.32604/sdhm.2025.068822

Received 06 June 2025; Accepted 11 August 2025; Issue published 17 November 2025

Abstract

During the operation, maintenance and upkeep of concrete buildings, surface cracks are often regarded as important warning signs of potential damage. Their precise segmentation plays a key role in assessing the health of a building. Traditional manual inspection is subjective, inefficient and has safety hazards. In contrast, current mainstream computer vision–based crack segmentation methods still suffer from missed detections, false detections, and segmentation discontinuities. These problems are particularly evident when dealing with small cracks, complex backgrounds, and blurred boundaries. For this reason, this paper proposes a lightweight building surface crack segmentation method, HL-YOLO, based on YOLOv11n-seg, which integrates an attention mechanism and a dilation-wise residual structure. First, we design a lightweight backbone network, RCSAA-Net, which combines ResNet50, capable of multi-scale feature extraction, with a custom Channel-Spatial Aggregation Attention (CSAA) module. This design boosts the model’s capacity to extract features of fine cracks and complex backgrounds. Among them, the CSAA module enhances the model’s attention to critical crack areas by capturing global dependencies in feature maps. Secondly, we construct an enhanced Content-aware ReAssembly of FEatures (ProCARAFE) module. It introduces a larger receptive field and dynamic kernel generation mechanism to achieve the reconstruction and accurate restoration of crack edge details. Finally, a Dilation-wise Residual (DWR) structure is introduced to reconstruct the C3k2 modules in the neck. It enhances multi-scale feature extraction and long-range contextual information fusion capabilities through multi-rate depthwise dilated convolutions. The improved model’s superiority and generalization ability have been validated through experiments on the self-built dataset. Compared to the baseline model, HL-YOLO improves mean Average Precision at 0.5 IoU by 4.1%, and increases the mean Intersection over Union (mIoU) by 4.86%, with only 3.12 million parameters. These results indicate that HL-YOLO can efficiently and accurately identify cracks on building surfaces, meeting the demand for rapid detection and providing an effective technical solution for real-time crack monitoring.

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Keywords

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