Abstract
Automated crack detection technologies based on deep learning have been extensively used as one of the indicators of performance degradation of concrete structures. However, there are numerous drawbacks of existing methods in crack segmentation due to the fine and microscopic properties of cracks. Aiming to address this issue, a crack segmentation method is proposed. First, a pyramidal residual network based on encoder−decoder using Omni-Dimensional Dynamic Convolution is suggested to explore the network suitable for the task of crack segmentation. Additionally, the proposed method uses the mean intersection over union as the network evaluation index to lessen the impact of background features on the network performance in the evaluation and adopts a multi-loss calculation of positive and negative sample imbalance to weigh the negative impact of sample imbalance. As a final step in performance evaluation, a dataset for concrete cracks is developed. By using our dataset, the proposed method is validated to have an accuracy of 99.05% and an mIoU of 87.00%. The experimental results demonstrate that the concrete crack segmentation method is superior to the well-known networks, such as SegNet, DeeplabV3+, and Swin-unet.