Crack Detection of Concrete Pavement Based on Attention Mechanism and Lightweight DilatedConvolution

Abstract

Cracks in the concrete pavement will affect the safety,applicability,and durability of the structure,and crack detection is a challenging research hotspot.This paper proposes a crack detection model composed of an improved full convolutional network and a deep supervision network,which uses the improved VGG-16 as the backbone network.Firstly,the low-level convolutional feature aggregation is fused to the backbone network again through the spatial attention mechanism.Secondly,the middle and high-level convolutional features are fused through the lightweight dilated convolution fusion module for multi-feature fusion to get the clear edge and high-resolution feature maps,all side feature maps are added to produce the final prediction map.Finally,the deep supervision network provides direct supervision for the detection results of each stage.In this paper,the focus loss function is selected as the evaluation function,and the trained network model can efficiently identify the crack location from the input original image under various conditions such as uneven illumination and complex background.To verify the effectiveness and robustness of the proposed method,it is compared with six methods on three datasets,DeepCrack,CFD,and Crack500,and the results show that it has excellent performance,and the F-score value reaches 87.12%.