Accurate detection of drowning victims in open water remains a major challenge for search-and-rescue (SAR) operations due to low illumination, reflections, occlusions, and complex backgrounds that degrade human visual performance. This study proposes a multi-modal AI-assisted UAV system for real-time drowning detection using a multi-rotor platform (<15 kg) equipped with integrated visual, thermal, and distance sensing, along with geolocation capabilities. A deep learning-based detection model was trained on 7103 images collected from real human subjects simulating four drowning scenarios in riverine and coastal environments, with additional stabilization and preprocessing modules to improve data quality. The proposed system achieves 98% detection accuracy, with a mean Average Precision (mAP@0.5) of 0.991 and a peak F1-score of 0.97. Results demonstrate reliable detection performance under challenging conditions, including low light, reflective water surfaces, and complex backgrounds, and show improved identification of low-contrast targets such as dark-clothed victims. These findings indicate that the proposed system provides a robust and scalable solution for real-time aquatic SAR applications and enhances the effectiveness of UAV-assisted rescue operations.
Cheng et al. (Wed,) studied this question.