Research on Building Crack Detection Based on YOLOv11-Seg

DOI: https://doi.org/10.62517/jcte.202506310

Author(s)

Xiaoying An*, Siqin Shi, Xiaofeng Wu, Chenguang Ma, Kezierkailedi Bahezhuoli

Affiliation(s)

China Institute of Building Standard Design & Research, Beijing, China *Corresponding Author

Abstract

To investigate the performance of convolutional neural network models in the field of building crack detection, this study selected the lightweight and efficient YOLOv11-seg model for object detection experiments on building cracks. By using polygons to annotate cracks at the pixel level, the model calculated experimental results for both bounding boxes and masks. The results show that the accuracy and recall rate of object detection both exceed 70%, indicating that the YOLOv11-seg model performs well in detecting building cracks. When the IoU threshold is 0.5, the model achieves a mean average precision (mAP) of over 75%, demonstrating its strong recognition capability for building cracks under moderate precision requirements. However, when the IoU threshold ranges from 0.5 to 0.95, the average mAP significantly decreases, and the mAP for bounding box prediction is notably higher than that for mask prediction, indicating that the model's accuracy in predicting crack edges is insufficient. These experimental results suggest that the YOLOv11-seg model can rapidly locate building cracks but still requires improvements in the precise detection of crack edges.

Keywords

Yolov11-Seg; Cracks; Object Detection

References

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