Aiming at the difficulty of detection of pavement diseases caused by the variety of types and scales of road pavement cracks， a lightweight unmanned aerial vehicle image crack detection method based on the GhostNet was proposed for the detection of different types of cracks in pavement. First， the Ghost module in the lightweight GhostNet was introduced to optimize the YOLOv4 backbone feature extraction network， and the lightweight model YOLOv4-Light was obtained， thereby reducing the model complexity and improving the crack detection speed. Then， the Efficient Channel Attention （ECA） mechanism was integrated in the model prediction output to further enhance the crack feature extraction ability and improve the precision of crack detection. Simulation results show that compared with the existing YOLOv4， the proposed method has the model size reduced by 82.31%， the amount of model parameters reduced by 82.56%， and the crack detection efficiency improved. The method can meet the detection needs of different types of cracks during road transportation.