This study investigates the use of deep learning algorithms for anomaly detection on small wind turbine blades. The research focuses on five base deep learning architectures—Xception, ResNet-50, AlexNet, VGG-19, and a custom convolutional neural network (CNN)—along with transfer learning approaches. A new dataset of 6000 RGB images was created, containing images of small wind turbine blades with simulated damage such as cracks, holes, and edge erosion. The dataset was generated using both indoor and outdoor images of a small wind turbine. Each model was tuned using different layers, image augmentations, and hyperparameter tuning to achieve optimal performance. The results showed that the proposed Transfer Xception model outperformed other architectures, achieving 99.92% accuracy on the test data of this dataset. Additionally, the model was tested on a dataset containing images of large-scale wind turbine blades, where Transfer Xception achieved 100% accuracy on the test data. These results indicate the effectiveness of deep learning in wind turbine blade fault identification. The study also explored the use of transfer learning techniques for wind turbine fault classification. The transfer learning approach involved using pretrained weights from ImageNet to improve model performance. The results demonstrated that transfer learning significantly improved accuracy and convergence speed compared to training from scratch. The research highlights the potential of deep learning in improving the accuracy and efficiency of wind turbine blade inspection and maintenance. The proposed methods show promising results in detecting faults and damages on wind turbine blades, which can help reduce maintenance costs and improve safety. Future work will focus on fault localization, fault size estimation, and the use of larger industrial-grade drones for autonomous inspections.This study investigates the use of deep learning algorithms for anomaly detection on small wind turbine blades. The research focuses on five base deep learning architectures—Xception, ResNet-50, AlexNet, VGG-19, and a custom convolutional neural network (CNN)—along with transfer learning approaches. A new dataset of 6000 RGB images was created, containing images of small wind turbine blades with simulated damage such as cracks, holes, and edge erosion. The dataset was generated using both indoor and outdoor images of a small wind turbine. Each model was tuned using different layers, image augmentations, and hyperparameter tuning to achieve optimal performance. The results showed that the proposed Transfer Xception model outperformed other architectures, achieving 99.92% accuracy on the test data of this dataset. Additionally, the model was tested on a dataset containing images of large-scale wind turbine blades, where Transfer Xception achieved 100% accuracy on the test data. These results indicate the effectiveness of deep learning in wind turbine blade fault identification. The study also explored the use of transfer learning techniques for wind turbine fault classification. The transfer learning approach involved using pretrained weights from ImageNet to improve model performance. The results demonstrated that transfer learning significantly improved accuracy and convergence speed compared to training from scratch. The research highlights the potential of deep learning in improving the accuracy and efficiency of wind turbine blade inspection and maintenance. The proposed methods show promising results in detecting faults and damages on wind turbine blades, which can help reduce maintenance costs and improve safety. Future work will focus on fault localization, fault size estimation, and the use of larger industrial-grade drones for autonomous inspections.