Aircraft skin defect detection algorithm based on enhanced YOLOv8

In order to solve the problem that traditional aircraft skin defect detection relies on human eye observation, which leads to reduced efficiency due to easy fatigue of the human eye and limited individual cognition, an aircraft skin defect detection algorithm based on improved YOLOv8 is proposed. Improve the data improvement strategy and propose a new one that combines slice reasoning with mosaic. Integrate the residual block into the feature extraction network to enhance the network expression ability and improve the accuracy of the model in aircraft skin defect detection tasks. Use the triplet attention module to strengthen the feature fusion network and lower the false and missed detection rates of small target samples. Optimize the structure of the detection head so that the network can better effectively combine shallow information with depth information. On the aircraft skin defect data set, experimental results indicate that the revised algorithm’s mean average precision (mAP) and recall rate have increased by 3.6% and 3.7%, respectively, in comparison to the most recent YOLOv8 algorithm. The mAP and recall rate on the public data set VOC2007 increased by 2.9% and 2.2%, respectively.