Abstract
Unmanned aerial vehicles (UAVs) combined with artificial intelligence can automate the inspection of wind turbine blades. This benchmark and deployment study compares lightweight YOLO detectors (YOLOv8, YOLO11, YOLO26; nano and small sizes; AdamW and SGD optimizers) trained for single-class damage detection on 5763 blade images and deployed on a Raspberry Pi 4. Each configuration was trained with three seeds and evaluated on a test split (mean ± standard deviation). At 640 × 640 pixels the six SGD configurations reached 75.5–76.8% mAP@50 after quantization and cannot be ranked by accuracy; with the default hyperparameters SGD gave a higher recall than AdamW in all 18 seed-matched pairs. Mixed-precision post-training quantization (backbone and neck INT8, detection head FP16) lowered mAP@50 by 0.8 percentage points on average and made the model files about three times smaller, but on the Raspberry Pi 4 it increased the latency by 1.5–2.2 times compared with FP32. YOLO26n-SGD, the fastest nano model, reached, in FP32, 9.9 FPS, but only 70.5 ± 0.5% mAP@50 at 320 × 320 (model trained at that size), against 2.6 FPS and 77.0 ± 0.9% at 640 × 640. SSDLite320 and EfficientDet-Lite0 reached 25.2% and 40.1% mAP@50. Flight tests and power consumption were not examined.