Detection of Delamination in Composite Laminate Using Mode Shape Processing Method and YOLOv8

Abstract

In this study, a novel delamination detection method for composite materials is proposed through the innovative use of You Only Look Once v8 (YOLOv8), vibration analysis, and 2D continuous wavelet transform techniques. The method detects the location and size of damage more accurately than existing methods and avoids manual intervention in the detection process. Damage detection performed on the simulation dataset shows that the method is able to accurately identify the delamination location with IoU = 0.9906 and an average accuracy of 91.32%. The proposed method is then compared with the widely used YOLOv5 model, and the superior performance of the YOLOv8 model is verified, with a 37.93% improvement in training speed and 0.81% improvement in detection accuracy. In addition, an experimental dataset of four composite laminates with delamination damage is constructed. By using transfer learning, the performance of the pretrained network achieves a good precision up to 1. The method proposed in this study expands the range of tasks that can be accomplished by mode shape analysis and is very effective in real experiments.