## 2 A Hands-On Project for Machine Learning and Autonomous Drones
Computer vision allows the analysis of visual data captured by the drone, which is essential for detecting and classifying objects, performing anomaly detection, and integrating AI to enhance the comprehension of autonomous systems. These technologies are deeply interconnected. For example, real-time computer vision is crucial for identifying and avoiding obstacles, a fundamental aspect of effective path planning. In target-tracking applications, computer vision supplies essential data for path-planning algorithms to dynamically adjust the drone's trajectory. Furthermore, computer vision facilitates the creation of detailed environmental maps, which path-planning algorithms utilize to optimize navigation routes. Students undertake various technical tasks throughout this project, including programming drones to control the behavior and maneuvers, developing and implementing path-planning and computer vision algorithms, and designing and conducting experiments to test drone capabilities. These tasks are designed to enhance problem-solving skills, technical proficiency, critical thinking, and collaboration and communication abilities. Through these activities, students gain hands-on experience with cutting-edge technologies, analyze and interpret data to make informed decisions, and work in teams to effectively present their findings. This comprehensive approach ensures that students are well prepared for careers in the field of drones and autonomous systems. Optimizing the path planning for drone inspections of PFBs unlocks numerous advantages, crucially enhancing the inspection process. Key among these is achieving expansive inspection coverage, where path-planning algorithms guide drones on optimal routes to encompass entire wind farms thoroughly. This meticulous approach ensures a comprehensive inspection, significantly mitigating the risk of overlooked turbines and minimizing redundant coverage areas. Additionally, the strategic optimization of path planning plays a pivotal role in enhancing operational efficiency. Drones can avoid unnecessary detours by navigating paths that account for distance and prevailing weather conditions, conserving flight time and battery life. This efficiency streamlines the inspection process and considerably reduces cost, rendering wind turbine inspections more economically feasible. With the addition of computer vision, drone adaptability increases, allowing real-time flexibility during the inspection process. This ensures drones operate securely within the complex and changing airspace of wind farms. In our previous work, simulated annealing was deployed to allow efficient paths to be generated for various combinations of PFBs; Haar-cascade classifiers were leveraged for pedestal fans' (in replacement of wind turbines) identification and positioning; the Hough transform was utilized for further vision positioning during the inspection mission [1]. Building on this work, the investigation of deep learning for fault analysis is presented to complete the proposed surrogate project and provide a comprehensive educational approach to learning these emergent technologies. Here, the automation of wind turbine inspection is of great interest as traditional approaches are time-consuming and often dangerous. These methods include manual inspection, requiring personnel to scale turbine blades, or visual inspection using various imaging and telescopic devices. As wind turbine installations continue to scale worldwide, the need for more efficient and safe inspection is ever-present. Through imagery captured with the autonomous path-planning algorithms presented in [1], various architectures are trained and compared on the ability to determine fault status in these PFBs. Coupled with autonomous path planning, this automated classification of faults completes the inspection system and provides an educational setting to explore these deep learning models.