Comparative Analysis of Defect Detection Models for Wind Turbine Structures

This paper presents the investigation and results of the training and use of the popular object detectors YOLOv8 and YOLOv11 in the identification of anomalies during the painting process of wind turbines. Several data augmentation techniques have been performed on the original dataset to assess its impact during the training process and the evaluating metrics of the proposed object detectors. The classification process has been carried out through binary scenarios to determine how the data augmentation process can enhance the use of neural network behavior in real-time applications. The aim of this paper is to quantify and evaluate how the introduction of new data augmentation enhances the performance of existing state-of-the-art models relying on data augmentation and labelling techniques.