基于全U网络的混凝土路面裂缝检测算法

针对现有的混凝土裂缝检测算法在各种复杂环境中检测精度不够、鲁棒性不强的问题,根据深度学习理论和U-net模型,提出一种全U型网络的裂缝检测算法。首先,依照原U-net模型路线构建网络;然后,在每个池化层后都进行一次上采样,恢复其在池化层之前的特征图规格,并将其与池化之前的卷积层进行融合,将融合之后的特征图作为新的融合层与原U-net网络上采样之后的网络层进行融合;最后,为了验证算法的有效性,在测试集中进行实验。结果表明,所提算法的平均精确率可达到83.48%,召回率为85.08%,F1为84.11%,相较于原U-net分别提升了1.48%,4.68%和3.29%,在复杂环境中也能提取完整裂缝,保证了裂缝检测的鲁棒性。

Concrete Pavement Crack Detection Algorithm Based on Full U-net

Aiming at the problems of insufficient precision and robustness of the existing crack detection algorithms in complex environments,a new model full U network is proposed based on the deep learning theory and U-net model.Firstly,the network is constructed based on the U-net model.Then,an upsampling is performed at every pooling layer to restore the feature map specification before this pooling layer and fuse it with the convolution layer before pooling.Finally,the new feature map is concatenated with the layer after upsampling on the U-net.In order to verify the effectiveness of the algorithm,experiments are performed on the test set.Experimental results show that the average precision of the proposed algorithm can reach 83.48%,the recall rate is 85.08%,and F1 is 84.11%.They are 1.48%,4.68%,3.29%higher than the precision,recall,and F1 in U-net respectively.It shows that in a complex environment,the full U network can still extract complete cracks and ensure the robustness.