In response to the reflective nature and complex surface defects of aircraft engine blades,this pa-per proposes an improved aircraft engine blade surface defect detection method based on an attention mech-anism and enhanced YOLOv8s.By replacing EIoU with CIoU as the algorithm's loss function.Simultane-ously,improvements were made in enhancing the accuracy of boundary box regression and target localiza-tion,while addressing the issue of data quality imbalances.The integration of an EMA attention module within the main feature network ( Backbone ) aimed to amplify the extraction of crucial features and en-hance the model's detection accuracy.The network was trained and tested using a proprietary dataset of air-craft engine blades.Experimental results demonstrate that the average detection accuracy of the YOLOv8s-EMA network reached 98.7％.Compared to current mainstream target detection models such as Faster-RC-NN and YOLOv5s,the average detection accuracy improved by 2.1％ and 3.0％ respectively,with a sig-nificant increase in FPS.This evidence validates the method's higher precision in detecting surface defects on aircraft engine blades,achieving commendable detection results.
维普
万方数据