Maintenance is a critical element of wind energy generation, ensuring not only the efficient operation of wind turbines but also their continuous availability and functionality. This is particularly important in the context of wind turbine blades (WTBs), where regular maintenance helps in maximizing energy output and minimizing downtime. There are extensive surveys covering various forms of maintenance, ranging from Machine Learning approaches for predictive maintenance to visual inspection techniques. However, a notable gap exists in the literature regarding visual inspection, particularly in the use of aerial imagery for \Rone{detecting defects} in WTBs. This gap stems from the unique challenges associated with this method. The objective of this review paper is to address this gap by focusing on the challenges and requirements for effective surface defect detection in WTBs through aerial imagery. \Rfive{The task of inspecting surface defects on WTBs is particularly difficult due to data scarcity, the need for substantial computational resources, and the geometric complexities in accurately localizing defects.} To address these issues, we explore the dynamics of the turbine environment and examine the intricacies involved in precisely identifying surface defects. Furthermore, we aim to identify and propose future promising directions to address the challenges at hand, thereby ensuring a continuum of research and development in this field.

I. Gohar, A. Halimi, W. K. Yew and J. See, "Review of state-of-the-art surface defect detection on wind turbine blades through aerial imagery: Challenges and recommendations,"  Engineering Applications of AI, 2025.

The processing of aerial images taken by drones is a challenging task due to their high resolution and the presence of small objects. The scale of the objects varies diversely depending on the position of the drone, which can result in loss of information or increased difficulty in detecting small objects. To address this issue, images are either randomly cropped or divided into small patches before training and inference. This paper proposes a defect detection framework that harnesses the advantages of slice-aided inference for small and medium-size damage on the surface of wind turbine blades. This framework enables the comparison of different slicing strategies, including a conventional patch division strategy and a more recent slice-aided hyper-inference, on several state-of-the-art deep neural network baselines for the detection of surface defects in wind turbine blade images. Our experiments provide extensive empirical results, highlighting the benefits of using the slice-aided strategy and the significant improvements made by these networks on an ultra high-resolution drone image dataset.

I. Gohar, A. Halimi, J. See,    W. K. Yew, C. Yang , "Slice-Aided Defect Detection in Ultra High-Resolution Wind Turbine Blade Images", Machines, 2023.  Code

Wind turbine blade (WTB) surface defect detection often suffers from severe class imbalance and limited annotated data, making conventional deep learning approaches impractical. Few-shot object detection (FSOD) addresses this challenge by enabling models to detect novel defect types from only a few labelled examples. In this study, FSOD is applied to the WTB surface defect detection task using the DTU dataset. We adopt the Two-Stage Fine-Tuning Approach and utilised Contrastive Proposal Encoding (CPE) loss to improve proposal discrimination and feature representation under low-data conditions. A progressive experimental setup is designed by partitioning defect categories into base and novel classes based on sample scarcity.  Our results show that integrating CPE loss leads to up to 22\% improvement in novel class detection and overall gains of around 2–3\% in mAP under 10-shot scenarios, while highlighting performance trade-offs under extreme class imbalance.These findings validate the effectiveness of contrastive objectives in FSOD and underscore the importance of strategic dataset construction for robust generalisation.

I. Gohar, A.Halimi, W. K. Yew and J. See, "Addressing Class Scarcity and Imbalance for Few-Shot Detection of Wind Turbine Blade Surface Defects", ISPACS, Indonesia, Nov. 2025.