融合多尺度混合注意力与迁移学习的全卷积网络路面裂缝检测算法OA

To address the limitations of conventional crack detection algorithms,such as multi-scale feature loss and high sensitivity to background interference in complex pavement scenarios,this paper proposes a novel pavement crack detection algorithm named HA-FCN-TL.The algorithm is based on a Fully Convolutional Network(FCN)in-tegrated with multi-scale Hybrid Attention(HA)and Transfer Learning(TL).First,an FCN backbone is construc-ted using a pre-trained ResNet34 model,where the transfer learning strategy accelerates model convergence and en-hances feature representation.Second,a hybrid attention module is designed to integrate Convolutional Block Atten-tion Module(CBAM)with self-attention during the encoding stage,achieving a synergistic optimization that en-hances microscopic crack edges while preserving macroscopic topological continuity.This effectively suppresses noise interference from pavement stains,uneven illumination,and other disturbances.Finally,a multi-scale feature fusion mechanism is introduced,employing skip connections to aggregate shallow details and deep semantic informa-tion across layers.Experiments on the DeepCrack dataset demonstrate that the proposed method excels in fractured texture repair and the detection of weak cracks,providing a highly robust solution for pavement structural safety as-sessment in complex environments.