## 1 Introduction  
Wind energy is gaining popularity as an environmentally friendly and sustainable substitute for the use of fossil fuels. As a result, wind farm construction has increased dramatically in recent years all over the world. Yet, maintaining these offshore or onshore wind turbines, particularly in distant places, remains a difficult undertaking. Wind turbine inspections traditionally need a professional crew to do manual inspections using rope climbing or ground equipment. Yet, rope-based inspection may be exceedingly dangerous for maintenance employees, and telephotography is sometimes unproductive, since microscopic fractures and damage are sometimes imperceptible to the human eye. Moreover, a typical examination necessitates substantial operating expenses and lengthy downtime.  
UAV-based inspection offers a lot of potential for wind turbine maintenance because of its high mobility, ease of deployment, and cheap maintenance cost. The usage of these UAV-based systems ensures operator safety and requires less downtime for inspection. In particular, UAV-based solutions can reduce inspection time compared to manned inspection. Furthermore, for wind turbines located in severe environments, regular inspection may be accomplished by obtaining high-definition photos or videos via loaded camera sensors for precise fracture, impairment, and deterioration analysis. As a result, it is easy to observe that UAV-based surveys are a more powerful and economical strategy that outperforms conventional inspection techniques.  
In the previous decade, quite a number of studies were conducted concentrating on UAV-based independent surveys for wind turbines at rest, and the application of deep learning techniques for damage detection [1; 2; 3; 4]. The basic concept is to guide the UAV through the set of predetermined inspection control points provided, using detection algorithms concentrating on lines such as the Hough transform on the basis of uncomplicated blade geometry (i.e., blades are viewed as line segments). Yet, because of their flexibility, blades are frequently missed during UAV inspection process. When checking in direct proximity to the blade (e.g., 10 m), disregarding such a nonlinear component might result in losing sight of the blade (particularly around the tip) if pursuing a set inspection route. As a result, from a practical standpoint, it is critical to consider the curvature of the blade. Deep learning techniques such as a Convolutional Neural Network (CNN) and Mask R-CNN have also been utilized to improve detection performance [5; 6].  
Multiple failures in wind turbine generator blades can result in a variety of repercussions, including decreased energy output, economic losses, equipment damage, human accidents, and so on.  
In this article, we present a deep learning vision-based technique for identifying specific defects on wind turbine blades. The proposed model is trained and tested exclusively for damage detection and evaluated against existing deep learning techniques for defects detection. Furthermore, ablation studies are performed to further improve the proposed technique's back-bone classification model. To this end, we assembled a collection of photos comprising the sorts of damage investigated in this study. The images are captured from a 110-MWac South African wind farm by means of a planned UAV-based drone inspection during bi-annual maintenance inspections of the wind turbine generators.  
The balance of the paper is structured in the following manner: Section 2 explains the related study, Section 3 discusses the UAV specification and wind farm specification, Section 4 describes the vision-based technique employing convolutional neural networks, and Section 5 describes and discusses experimentation and findings. Finally, Section 6 brings the process to a close.