## 6 Real-World Challenges and Alternative Data Acquisition Strategies  
Implementing drone-based inspection systems for PFBs or similar applications in real-world environments presents several challenges. For example, when it comes to wind turbine blade (WTB) inspection using drones, adverse weather conditions such as rain, snow, fog, and strong winds can severely impact the drone's flight stability and image quality [67]. Ensuring the drone can operate in various weather conditions or developing protocols for data collection under optimal weather conditions is crucial. Additionally, proximity to large rotating wind turbine blades can create turbulence, posing a risk to the drone's stability and safety [68]. Implementing advanced control algorithms and stabilizing mechanisms can mitigate this risk. Real-world environments may also present obstacles such as electric lines, trees, and buildings that can obstruct the drone's path and pose collision risks [69]. Therefore, advanced obstacle-detection and -avoidance systems are essential for safe navigation. Acquiring a sufficient amount of labeled training data can be another significant challenge. In real-world scenarios, manual annotation of images can be time-consuming and labor-intensive, making it difficult to build large datasets [70].  
Figure 23: Misclassified images by VGG.  
Figure 24: Misclassified images by AlexNet.  
To address the challenge of acquiring training data in real-world applications, several alternative strategies can be employed. Utilizing computer-generated images to augment the training dataset can be an effective approach. Techniques such as generative models can create realistic synthetic images that help diversify the training data [71]. Applying various data-augmentation techniques such as rotation, scaling, flipping, and color adjustments can artificially expand the training dataset, improving the robustness and generalization of the model [72]. Leveraging pre-trained models on large publicly available datasets can significantly reduce the amount of required training data [73]. Fine-tuning these models on a smaller domain-specific dataset can yield effective results. Engaging a community of users or using platforms like Amazon's Mechanical Turk can facilitate the annotation process by distributing the workload across many contributors [74]. Combining labeled and unlabeled data in a semi-supervised learning framework can also improve model performance. Techniques such as self-training and co-training can utilize the unlabeled data to enhance the learning process [75].  
In real-world applications, acquiring images directly from the blades and annotating them can indeed serve as an immediate assessment of damage. However, for educational purposes and model training, it is crucial to maintain a separate dataset and annotation process to ensure the model can learn to generalize from labeled examples. While the annotation process might seem redundant, it is necessary for training robust models capable of performing automated inspections independently. By addressing these challenges and employing alternative data-acquisition strategies, we aimed to provide a comprehensive framework that prepares students and instructors for real-world applications while ensuring the effectiveness and reliability of drone-based inspection systems.  
The subsequent section details the study's methodology, focusing on drone path-planning and image-processing techniques. It explains the algorithms and technologies used to optimize drone navigation for efficient PFB inspections and the image-processing methods for detecting and classifying anomalies. The aim is to demonstrate how advanced path planning and image processing enhance the effectiveness and reliability of drone-based inspections, highlighting both the technical aspects and practical applications.