An advanced three stage lightweight model for underwater human detection

Le résumé fourni par la source

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.

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