Abstract

The efficient and accurate detection of cracks in concrete structures is critical for maintaining structural integrity and safety. This study compares two state-of-the-art convolutional neural network (CNN) models, Mask R-CNN and YOLOv8, for automated concrete crack detection, each model representing two mainstream approaches for object detection and instance segmentation: single-stage and two-stage approach. We evaluate both models on 1,203 concrete images with 7:2:1 training, testing, and validation split, and assess their accuracy and processing speed. Mask R-CNN achieves a mean Intersection over Union (IoU) of 96.5% with a minimum IoU of 77% and higher consistency, compared to YOLOv8’s 90.6%, which often shows complete failure with IoU of 0%. In terms of computation speed, YOLOv8 shows 0.3225 s of average processing time per image, slightly outperforming the speed of Mask R-CNN, 0.4867 s. Despite YOLOv8’s faster processing speed, considering the characteristics of concrete crack detection tasks where accuracy should be prioritized over speed, Mask R-CNN seems a more proper model for reliable crack detection. We also show the accuracy of Mask R-CNN for crack detection tasks can be further enhanced by employing the ResNeXt backbone.

Publications

• Y. Choi, B. Bae, T. H. Han and J. Ahn, "Application of Mask R-CNN and YOLOv8 Algorithms for Concrete Crack Detection," in IEEE Access, doi: 10.1109/ACCESS.2024.3469951.