Aircraft skin defect detection algorithm based on improved YOLOv8

Abstract

To solve the problem that the traditional aircraft skin defect detection relies on human eye observation, which leads to reduced efficiency due to human eye fatigue and limited individual cognition, an aircraft skin defect detection algorithm based on improved YOLOv8 is proposed. The data enhancement method is improved, and a slice reasoning + mosaic data enhancement method is proposed; the residual block is integrated into the feature extraction network to enhance the network expression ability and improve the accuracy of the model in the aircraft skin defect detection task; the three-branch (Triplet) attention module is applied to improve the feature fusion network to reduce the false detection rate and missed detection rate of small target samples; the structure of the detection head is optimized so that the network can better combine shallow information with deep information. Experimental results show that compared with the latest YOLOv8 algorithm, the improved algorithm improves the mean average precision (mAP) and recall rate (Recall) on the aircraft skin defect dataset by 3.6%and respectively 3.7%; and improves the mean average precision and recall rate on the public dataset VOC2007 by 2.9%and 2.2%.