A Vision-Based Deep Learning Framework for Autonomous Inspection and Damage Assessment of Wind Turbine Blades Using Unmanned Aerial Vehicles

2.1 The imperative for advanced blade inspection

The integrity of wind turbine blades is a linchpin for wind farm profitability and safety. Studies have consistently shown that blade failures are among the costliest events in a wind farm's operational lifecycle [4, 16]. The economic drivers for advanced inspection are clear, moving from reactive to predictive and prescriptive maintenance paradigms to optimize operational expenditures (OPEX) and maximize availability [17, 18]. The types of damage are varied and complex. Leading-edge erosion (LEE), caused by impact from rain, hail, dust, and insects, disrupts airflow and is the most common defect, directly reducing annual energy production (AEP) [19]. Structural damages like cracks, delamination between composite layers, and bond-line failures can lead to major structural repairs or total blade loss if not detected early [20]. The economic driver for advanced inspection is clear, moving from reactive to predictive maintenance paradigms to OPEX [17].

2.2 Evolution of inspection techniques

The journey of wind turbine inspection has evolved through several generations:

•First Generation (Manual Inspection): Relied on ground-based observations or rope-assisted technicians. These methods are highly subjective, unsafe, and result in significant downtime [9].

•Second Generation (Remote Sensing): Employed techniques like thermography, ultrasound, and acoustic emission. While offering deeper insights, they often require complex setups and proximity to the blade, limiting their practicality for rapid, widespread use [21].

•Third Generation (Robotic and UAV-assisted): The advent of robotics and drones marked a significant advance. Robotic crawlers can provide detailed contact-based measurements but are slow and logistically challenging [10]. UAVs initially served as flying cameras, capturing visual data for later manual analysis by experts [15]. This reduced safety risks but still incurred significant data analysis latency.

2.3 Deep learning in computer vision

Deep learning, particularly CNNs, has revolutionized computer vision. Their ability to automatically learn hierarchical features from raw pixel data has led to breakthroughs in:

•Object Detection: Models like the YOLO (You Only Look Once) series [22] and Faster R-CNN [23] can identify and localize objects within an image with high speed and accuracy. This capability is crucial for a UAV to autonomously locate and track a turbine blade in a cluttered environment.

•Semantic Segmentation: Architectures such as U-Net [24] and DeepLab [25] perform pixel-wise classification, assigning a label to every pixel in an image. This is essential for precisely delineating the shape and extent of damage on a blade surface, providing quantitative data for severity assessment.

Looking beyond the architectures used in this work, the field of deep learning is rapidly evolving. Transformer architectures, which utilize self-attention mechanisms [26], have shown remarkable success in natural language processing and are increasingly being applied to vision tasks. Future inspection systems may leverage such models for even more powerful contextual understanding of blade defects.

2.4 AI and IoT in adjacent fields: A blueprint for wind energy

The proposed framework draws significant inspiration from successful applications of AI and IoT in other sectors, demonstrating the cross-domain potential of these technologies.

•Precision Agriculture: Research by Ezziyyani et al. [13], Rekiek et al. [27] and Rekiek et al. [28] have demonstrated the efficacy of CNN-based models for detecting plant diseases and pests from leaf imagery. Their work on high-accuracy classification in complex, unstructured environments provide a direct parallel to identifying blade damage patterns. Similarly, researches by Gouiza et al. [29[ and Gouiza et al. [30] have extensively reviewed and developed IoT frameworks for smart farming, emphasizing the role of sensor data fusion and remote monitoring, which mirrors the data acquisition challenges in wind farm management.

•Precision Livestock Farming: The studies [13, 31] have pioneered the integration of hybrid AI, IoT, and edge computing for real-time monitoring of animal health and welfare. Their work on deploying lightweight models on edge devices for instantaneous prediction [32] is directly relevant to the need for onboard processing on UAVs. The concept of continuous, autonomous monitoring for early anomaly detection is a core principle shared between livestock management and structural health monitoring.

•Broader IoT and AI Integration: The foundational reviews by Gouiza et al. [33] and Ezziyyani et al. [34] on the integration of IoT and AI across diverse domains provide a holistic view of the enabling technologies—sensor networks, communication protocols, and data analytics—that are equally critical for creating a digital twin of a wind farm through autonomous inspection systems.

2.5 Gap identification

While previous research has effectively used UAVs for data collection and applied deep learning to analyze blade imagery offline, a critical gap remains in creating a truly autonomous, edge-native system. Most existing solutions decouple data acquisition from analysis [14]. A system that seamlessly integrates real-time navigation, obstacle avoidance, and instantaneous damage diagnosis onboard the UAV represents the next evolutionary step, minimizing human intervention, reducing latency from data transmission, and enabling immediate action. This research aims to bridge this gap by proposing and validating such an integrated framework.

4.1 Performance metrics

We evaluated our models using standard computer vision metrics:

•Object Detection (YOLOv7): Mean Average Precision (mAP@0.5), Precision, Recall, F1-Score.

•Semantic Segmentation (DeepLabV3+): Mean Intersection over Union (mIoU), per-class IoU, Pixel Accuracy.

4.2 Experimental results

The YOLOv7 model demonstrated exceptional performance in detecting and localizing wind turbine blades in real-time, as summarized in Table 2.

Table 2. Performance of the YOLOv7 blade detection model on the test set

The high frame rate of 42 FPS on the Jetson Orin ensures that the UAV can react swiftly to changes in its environment, enabling stable and safe autonomous navigation around the moving turbine structure.

4.3 Damage segmentation performance

The DeepLabV3+ model achieved state-of-the-art results for the complex task of segmenting multiple damage types, as shown in Table 3.

Table 3. Semantic segmentation performance of the DeepLabV3+ model

The model shows high accuracy across all damage classes. The slightly lower IoU for Crack and Delamination is expected due to their thin, elongated shapes and similarity to blade seams, which presents a greater challenge for pixel-wise classification (Figure 5).

Figure 5. Segmentation performance by class

While the model performs well overall, a closer analysis reveals limitations, particularly with small or thin damage types. As seen in Table 3, the IoU for 'Crack' is the lowest among the damage classes. This is primarily due to the challenging nature of cracks, which often appear as thin, elongated features that can be as narrow as a few pixels wide. They are easily confused with blade surface scratches, manufacturing seams, or shadow edges. Similarly, 'Delamination' can be difficult to distinguish from surface discoloration or dirt.

Future work could address this by employing loss functions more sensitive to imbalanced class boundaries or by using higher-resolution input patches for these specific regions.

Figure 6 shows qualitative results of the model's segmentation output on sample images.

Figure 6. Qualitative damage segmentation results

A grid of images showing original photos, ground truth masks, and predicted masks from the DeepLabV3+ model for different types of damage.

4.4 Ablation study: The importance of multi-task learning

We conducted an ablation study to validate our architectural choices. A baseline model that used a single, larger network to perform both detection and segmentation was compared to our dual-network approach. The dual-network approach proved superior, as the specialized networks could be optimized for their specific tasks without compromise. The single network suffered from a 12% drop in mIoU for segmentation and a 15% drop in mAP for detection, confirming the efficacy of our proposed design (Figure 7).

Figure 7. Ablation study-impact of architecture choices

To contextualize the contributions of this work, a comparative analysis with representative existing systems is provided in Table 4. While other solutions offer partial capabilities, such as automated data collection via pre-programmed flights, our framework's integration of real-time, on-board navigation and semantic segmentation represents a significant step towards full autonomy and immediate insight generation.

Table 4. Comparative analysis with existing UAV-based inspection systems

4.5 Edge deployment performance

The final optimized pipeline, running simultaneously on the Jetson AGX Orin, achieved an overall system throughput of 8 FPS for the full workflow. The power consumption of the Jetson module under this full AI load was profiled at 15 Watts. Considering the DJI Matrice 300 RTK's typical total power draw of ~200-250 Watts during inspection flights and its standard battery capacity of 2,970 Wh, the additional load from the AI system accounts for a less than 8% increase in power consumption. This translates to a reduction in maximum flight time of approximately 4-5 minutes, which is acceptable given that standard blade inspection missions are typically planned for 15-20 minutes, leaving sufficient operational margin.

4.6 Deployment robustness and field testing

Beyond benchmark performance, the system's robustness was evaluated through extended field tests. Ten consecutive inspection missions were conducted, simulating a full day of operation. The system successfully completed all missions without requiring manual intervention. To test resilience, we simulated a temporary loss of the communication link to the Ground Control Station (GCS). The UAV correctly continued its pre-planned autonomous mission based on the last known command. Furthermore, a watchdog timer and a restart mechanism were implemented within the main ROS node. During testing, this system successfully detected and recovered from a simulated software crash in the AI pipeline within 15 seconds, resuming normal operation without necessitating a UAV landing. These tests confirm the system's suitability for prolonged, real-world inspection tasks.

4.7 Broader implications and future work

The successful demonstration of this autonomous inspection system has profound implications for the wind energy industry. It represents a paradigm shift from manual, periodic inspections to automated, continuous structural health monitoring. This capability is a critical enabler for the future of large-scale, especially offshore, wind farms where access is limited and costs are high.

The principles and technologies developed herein are highly transferable. The vision-based navigation system can be adapted for inspecting other critical infrastructure such as bridges, dams, and transmission lines. The damage segmentation model's architecture could be retrained for quality control in manufacturing processes for composite materials, a key technology in aerospace and automotive industries.

However, several challenges and opportunities for future work remain:

(1) Multi-Modal Sensor Fusion: Integrating thermal imaging data from the H20T camera could allow the detection of sub-surface defects like delamination that are not visible to the naked eye, providing a more comprehensive health assessment.

(2) 3D Reconstruction and Quantification: Using photogrammetry techniques from the captured images to create a 3D model of the blade would allow for precise quantification of damage volume and depth, providing even more accurate data for repair planning.

(3) Lifelong and Federated Learning: Implementing continuous learning mechanisms [35] would allow the model to adapt to new, unseen damage types over time. Furthermore, a federated learning approach could enable a fleet of inspection drones across multiple wind farms to collectively improve a global model without sharing sensitive data, enhancing overall intelligence [36, 37].

(4) Standardization and Regulation: For widespread adoption, the findings from autonomous systems must be integrated into industry-standard asset management platforms and approved by regulatory bodies. Developing automated reporting standards that align with current maintenance workflows is an essential next step.

(5) Automated Damage Severity Scoring: To enhance the practical utility for maintenance crews, a future direction is the development of an automated severity scoring system. This system would synthesize the segmentation outputs (damage type and area), morphological features (e.g., crack length-to-width ratio), and contextual information (e.g., proximity to the blade root or leading edge) into a unified severity index. For example, a large delamination near the blade root would be assigned a higher priority score than a small surface erosion near the tip, enabling more efficient and prioritized maintenance planning.

Looking beyond the architectures used in this work, the field of deep learning is rapidly evolving. Transformer-based models, such as SegFormer and Swin Transformer, which utilize self-attention mechanisms, have shown remarkable success in semantic segmentation tasks by capturing long-range dependencies and global context more effectively than traditional CNNs. Future inspection systems may leverage such models for even more powerful contextual understanding of blade defects, potentially improving the segmentation of subtle and complex damage patterns. A key research challenge will be to adapt these often computationally heavy models for efficient edge deployment.