Crack Detection Method for Asphalt Pavement Combining Multi-scale Features and Attention Mechanism

Abstract

Crack, as one of the common diseases of asphalt pavements, seriously affects the health of asphalt pavements. To cope with the demand of crack detection in the context of complex pavements, an improved network model with an encoder-decoder structure is proposed. First, dense long and short connections are combined with full-scale jump connections, and the jump connection structure yields fullscale feature information to each node of the decoding layer. Second, spatial and channel attention modules are incorporated into the proposed network. The former is used at the low level of the network to improve the ability to capture crack detail information, and the latter is applied at the high level of the proposed network to obtain the semantic information. Finally, improve network performance by building deep supervision network. The proposed network is compared with on three datasets, DeepCrack, CFD, and Crack500, and the F-score reaches 86.79%. In this paper, the network is effective in crack detection and plays a certain role in maintaining road safety.