Wind turbines, as critical components of the renewable energy industry, present unique maintenance challenges, particularly in remote or challenging locations such as offshore wind farms. These are amplified in the inspection of leading-edge erosion on wind turbine blades, a task still largely reliant on traditional methods. Emerging technologies like computer vision and object detection offer promising avenues for enhancing inspections, potentially reducing operational costs and human-associated risks. However, variability in image resolution, a critical factor for these technologies, remains a largely underexplored aspect in the wind energy context. This study explores the application of machine learning in detecting and categorizing leading edge erosion damage on wind turbine blades. YOLOv7, a state-of-the-art object detection model, is trained with a custom dataset consisting of images displaying various forms of leading edge erosion, representing multiple categories of damage severity. Trained model is tested on images acquired with three different tools, each providing images with a different resolution. The effect of image resolution on the performance of the custom object detection model is examined. The research affirms that the YOLOv7 model performs exceptionally well in identifying the most severe types of LEE damage, usually classified as Category 3, characterized by distinct visual features. However, the model's ability to detect less severe damage, namely Category 1 and 2, which are crucial for early detection and preventive measures, exhibits room for improvement. The findings point to a potential correlation between input image resolution and detection confidence in the context of wind turbine maintenance. These results stress the need for high-resolution images, leading to a discussion on the selection of appropriate imaging hardware and the creation of machine learning-ready datasets. The study thereby emphasizes the importance of industry-wide efforts to compile standardized image datasets and the potential impact of machine learning techniques on the efficiency of visual inspections and maintenance strategies. Future directions are proposed with the ultimate aim of enhancing the application of artificial intelligence in wind energy maintenance and management, enabling more efficient and effective operational procedures, and driving the industry towards a more sustainable future
