Zhang Wenhui, Chen Lu, Shi Jianchun
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
Jiangsu Zhaoming Information Technology Co., Ltd., Nantong 213000, China.
Sensors (Basel). 2025 Sep 5;25(17):5552. doi: 10.3390/s25175552.
Drowning constitutes the leading cause of injury-related fatalities among adolescents. In swimming pool environments, traditional manual surveillance exhibits limitations, while existing technologies suffer from poor adaptability of wearable devices. Vision models based on YOLO still face challenges in edge deployment efficiency, robustness in complex water conditions, and multi-scale object detection. To address these issues, we propose YOLO11-LiB, a drowning object detection model based on YOLO11n, featuring three key enhancements. First, we design the Lightweight Feature Extraction Module (LGCBlock), which integrates the Lightweight Attention Encoding Block (LAE) and effectively combines Ghost Convolution (GhostConv) with dynamic convolution (DynamicConv). This optimizes the downsampling structure and the C3k2 module in the YOLO11n backbone network, significantly reducing model parameters and computational complexity. Second, we introduce the Cross-Channel Position-aware Spatial Attention Inverted Residual with Spatial-Channel Separate Attention module (C2PSAiSCSA) into the backbone. This module embeds the Spatial-Channel Separate Attention (SCSA) mechanism within the Inverted Residual Mobile Block (iRMB) framework, enabling more comprehensive and efficient feature extraction. Finally, we redesign the neck structure as the Bidirectional Feature Fusion Network (BiFF-Net), which integrates the Bidirectional Feature Pyramid Network (BiFPN) and Frequency-Aware Feature Fusion (FreqFusion). The enhanced YOLO11-LiB model was validated against mainstream algorithms through comparative experiments, and ablation studies were conducted. Experimental results demonstrate that YOLO11-LiB achieves a drowning class mean average precision (DmAP50) of 94.1%, with merely 2.02 M parameters and a model size of 4.25 MB. This represents an effective balance between accuracy and efficiency, providing a high-performance solution for real-time drowning detection in swimming pool scenarios.
溺水是青少年伤害相关死亡的主要原因。在游泳池环境中，传统的人工监控存在局限性，而现有技术中可穿戴设备的适应性较差。基于YOLO的视觉模型在边缘部署效率、复杂水域条件下的鲁棒性以及多尺度目标检测方面仍面临挑战。为了解决这些问题，我们提出了YOLO11-LiB，一种基于YOLO11n的溺水目标检测模型，具有三项关键改进。首先，我们设计了轻量级特征提取模块（LGCBlock），它集成了轻量级注意力编码块（LAE），并有效地将Ghost卷积（GhostConv）与动态卷积（DynamicConv）相结合。这优化了YOLO11n主干网络中的下采样结构和C3k2模块，显著减少了模型参数和计算复杂度。其次，我们将具有空间通道分离注意力的跨通道位置感知空间注意力倒置残差模块（C2PSAiSCSA）引入主干。该模块将空间通道分离注意力（SCSA）机制嵌入到倒置残差移动块（iRMB）框架中，实现更全面、高效的特征提取。最后，我们将颈部结构重新设计为双向特征融合网络（BiFF-Net），它集成了双向特征金字塔网络（BiFPN）和频率感知特征融合（FreqFusion）。通过对比实验，将增强后的YOLO11-LiB模型与主流算法进行了验证，并进行了消融研究。实验结果表明，YOLO11-LiB的溺水类别平均精度（DmAP50）达到94.1%，参数仅为2.02M，模型大小为4.25MB。这在准确性和效率之间实现了有效平衡，为游泳池场景中的实时溺水检测提供了高性能解决方案。
