Marine drowning detection method based on improved YOLOv5

Wenqi Xue; Yuanjian Zhang

doi:10.1117/12.3034947

7 August 2024 Marine drowning detection method based on improved YOLOv5

Wenqi Xue, Yuanjian Zhang

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 132240O (2024) https://doi.org/10.1117/12.3034947
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

In this study, we developed a drowning detection method named AquaYOLO, which enhances the YOLOv5 framework by integrating the detection head mechanism from YOLOX. This involves decoupling the classification and regression prediction heads and incorporating a Dual Attention Net module that combines Channel Attention with Position Attention. Additionally, we added a small target detection layer with 4x4 pixel resolution. To improve border positioning accuracy, we replaced the conventional IoU loss function with the SIoU loss function. Our experiments demonstrate that AquaYOLO achieves a detection accuracy of 92.24% and a recall rate of 82.612% on the dataset, which is 9.98% higher than the traditional YOLOv5s. When compared to YOLOv3, YOLOv4, and Faster CNN, AquaYOLO shows superior detection accuracy, indicating its effectiveness in drowning detection scenarios. This study offers a significant advancement in marine drowning rescue solutions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenqi Xue and Yuanjian Zhang "Marine drowning detection method based on improved YOLOv5", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 132240O (7 August 2024); https://doi.org/10.1117/12.3034947

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