Construction cranes are the core equipment of modern engineering, and their high-risk operation at height is prone to cause major accidents and economic losses, seriously threatening safety. In order to improve the efficiency and accuracy of defect recognition and reduce the risk of operators climbing up to inspect, a surface defect intelligent detection method FRE based on UAV images is proposed. The surface defects of construction cranes are diverse, tiny in scale and complex in background, and the traditional YOLOv8 network is difficult to realize high-precision defect detection due to the lack of multi-scale feature fusion capability and the limitation of environmental adaptability. Utilizing the UAV inspection construction equipment, two typical lifting machine defect image datasets of wire rope defects and metal structure corrosion are established. The C2F module in the YOLOv8 backbone network is replaced with the RepViT Block module to improve the performance and efficiency of the model in image understanding and processing, which significantly reduces the computational complexity and latency, and the training speed is increased by 46.4% and 2.6%, respectively; the C2F module in the neck network is replaced by the FasterNet Block module, which improves the performance of the localization of defects and improves the ability of detecting small targets; the EMA module is embedded into the backbone network to suppress the interference of background information and make the model more focused on defect features. Compared with the existing defect detection, the detection accuracy of the model reaches 88.0% and 94.1%, respectively. Meanwhile, the number of model parameters decreased by 23.26% compared with the YOLOv8 model. The results show that the method can quickly and accurately detect the surface defects of construction cranes, which has certain social application value.