Crack Detection of the Urban Underground Utility Tunnel Based on Residual Feature Pyramid Attention Network

The defect detection in the urban underground utility tunnel faces the challenges of low illuminance and large shadow region. The images collected have problems such as noise and uneven illumination, posing higher requirements for the image feature extraction ability of the network model. A residual feature pyramid attention network based on dense connections (RFPADNet) is proposed in the defect detection of the urban underground comprehensive pipe in this paper. The proposed network consists of three dense blocks, and each dense block uses four residual feature pyramid attention models (RFPAM) as the main feature extractors. Its focus is to utilize residual learning to obtain the fused multi-scale feature information. Soft mask branches are added to the multi-scale channels to enhance the network model’s ability to extract positive features. The experimental results show that the network model proposed in this paper has a training accuracy of 97.50