In order to solve the issue of low efficiency in surface defect detection of gas turbine blades by using industrial endoscope

an algorithm based on improved YOLOv8 is proposed for turbine blade surface defect detection. Firstly

deformable convolutions are introduced into the backbone network

and the C2f_DCNv3 module is designed to enhance the model’s feature extraction capability. Secondly

the Shuffle Attention module is added to further improve the detection accuracy of the model. In addition

the GSCSP module is designed in the feature fusion part to reduce the model’s computation and enhance its detection capability for small targets. Finally

a dataset for turbine blade surface defects is established for model training and testing. Experimental results show that the improved model achieves a mean average precision of 92.3% for defect detection

which is a 2.1% improvement over the original YOLOv8 model. Moreover

the number of parameters and computation are reduced by 13% and 17% respectively. In comparison with other algorithms

the proposed algorithm in this study achieves better detection results and is more suitable for detecting surface defects on turbine blades.