Liao Zichen, Hu Kai, Meng Yuancheng, Shen Shuai
School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
University of Reading,Whiteknights, PO Box 217, Reading, Berkshire, RG6 6AH, UK.
Sci Rep. 2025 May 25;15(1):18137. doi: 10.1038/s41598-025-03677-2.
This study presents StarEye, a lightweight deep learning model designed for underwater human body detection (UHBD) that addresses the challenges of complex underwater environments. The proposed model incorporates several innovative components: a comprehensive underwater dataset construction methodology, a StarBlock-based backbone structure for efficient feature extraction, a Context Anchor Attention (CAA) mechanism integrated into both backbone and neck components, and a Shared Convolution Batch Normalization (SCBN) detection head. Extensive experiments demonstrate that StarEye achieves 91.1% precision, 88.6% recall, and 95.1% mAP50 while reducing the model size to 3.8MB (16.9% of the original size). The model maintains robust performance across various underwater conditions, including poor visibility, varying illumination, and biological interference. The results indicate that StarEye effectively balances model efficiency and detection accuracy, making it particularly suitable for mobile device deployment in underwater scenarios.
本研究提出了StarEye，这是一种为水下人体检测（UHBD）设计的轻量级深度学习模型，旨在应对复杂水下环境带来的挑战。所提出的模型包含几个创新组件：一种全面的水下数据集构建方法、一种基于StarBlock的主干结构以进行高效特征提取、一种集成到主干和颈部组件中的上下文锚点注意力（CAA）机制，以及一个共享卷积批归一化（SCBN）检测头。大量实验表明，StarEye的精度达到91.1%，召回率达到88.6%，mAP50达到95.1%，同时将模型大小减小到3.8MB（原始大小的16.9%）。该模型在各种水下条件下都保持了强大的性能，包括能见度差、光照变化和生物干扰。结果表明，StarEye有效地平衡了模型效率和检测精度，使其特别适合在水下场景中部署到移动设备上。
