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) integrated with multi-scale Hybrid Attention (HA) and Transfer Learning (TL).First,an FCN backbone is constructed using a pre-trained ResNet34 model,where the transfer learning strategy accelerates model convergence and enhances feature representation.Second,a hybrid attention module is designed to integrate Convolutional Block Attention Module (CBAM) with self-attention during the encoding stage,achieving a synergistic optimization that enhances 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 information 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 assessment in complex environments.