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...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.展开更多
基金support from Natural Science Foundation of Hunan Province(Grant No.2024JJ8055)Hunan Yiduoyun Commodity Itelligence Project(Grant No.h2024-003).
文摘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.
