To improve the current low efficiency process in artificial defect detection on aero-engine surface, a YOLOv5-CE model, based on improved YOLOv5, is proposed. First, the data enhancement strategy search algorithm is integrated into the network to automatically search the best data enhancement strategy for the current dataset to improve the training effect. Second, the coordinate attention mechanism is introduced into the backbone network while the coordinate information is embedded on the basis of channel attention to improve detection of small defect targets. Finally, the location loss function of YOLOv5 is improved to efficient intersection over union loss which can accelerate the model convergence and improve the precision of prediction box regression. Experimental results show that compared with the original YOLOv5s network, the proposed YOLOv5-CE model improves the mean average precision by 1.2 percentage points to 98.5% and can efficiently, as well as intelligently, detect four common types of defects in aero-engines.