U-Net的桥梁裂缝智能检测方法改进研究

Research on Improvement U-Net Intelligent Detection Method for Bridge Cracks

针对桥梁裂缝检测准确率与精度有待提高的问题,提出了基于图像分割技术的U-Net网络进行桥梁裂缝检测。采用西安市的桥梁裂缝数据集,通过人工标定完成了对数据集的标注,用于进行监督学习。针对这一数据集采用了数据增强、空洞卷积、批次归一化等方法提高识别精度,减少过拟合现象;将交叉熵损失与Dice损失相结合,提高了模型训练的速度以及识别精度。与广泛应用的图像分割方法进行比较,实验结果表明该模型在桥梁裂缝数据集上的分割表现结果具有优越性。

Aiming at the problem that the accuracy and precision of bridge crack detection need to be improved,a U-Net network based on image segmentation technology is proposed for bridge crack detection.The bridge crack data set in Xi'an city is used to mark the data set through manual calibration for supervision and learning.For this data set,data enhancement,void convolution,batch normalization and other methods are used to improve the recognition accuracy and reduce the over fitting phenomenon;the cross entropy loss and Dice loss are combined to improve the speed of model training and the recognition accuracy.Compared with the widely used image segmentation methods,the experimental results show that the model has advantages in the segmentation performance of the bridge crack dataset.