This article examines differences between conventional manual measurements of tram operations and data extracted automatically using the REWIZOR program, based on the Yolo10s algorithm. The study addresses the broader question of how artificial intelligence can support analyses of passenger exchange processes in public transport and improve the efficiency of data collection. Measurements conducted in four Polish cities included tram types, stop times, and detailed boarding and alighting durations, while the REWIZOR software enabled automatic detection of stop times and passenger flows based on video recordings. The results show that, although both approaches yield consistent qualitative information regarding doors and passenger counts, significant quantitative discrepancies arise. These differences stem mainly from methodological inconsistencies and varying definitions of boarding, alighting, and stop times, as well as from software-related detection errors. The findings indicate that AI-based measurements require calibration against reference methods to allow reliable comparison with conventional datasets. As currently implemented, REWIZOR can be used effectively for internal analyses of passenger flows, if all compared data come from the same system. Further development—such as implementing simultaneous tracking of people and heads—may considerably improve accuracy and facilitate wider applicability in public transport studies.

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.