Lightweight concrete crack segmentation method based on Deeplabv3+

Abstract

As one of the most serious damages of concrete structure, crack will lead to shorter service life and lower safety factors of concrete structure. The existing concrete crack segmentation model has the shortcomings of large number of parameters and slow inference speed, which makes it difficult to apply the model to the actual detection scene. To solve the above problems, this paper proposes a lightweight concrete crack segmentation method based on DeeplabV3+, which effectively reduces the number of model parameters and improves crack segmentation accuracy by replacing the backbone and adding the SSH (Single Stage Headless) context module and ECA (Efficient Channel Attention). The experimental results show that the crack segmentation Precision and IoU of the proposed model are superior to other models when comparing the unit parameter number, the crack segmentation Precision and IoU of the proposed model reach 80.80% and 71.45% respectively as well, when only accounting for 14.3% of the parameters of DeeplabV3+ with ResNet101 as the backbone.