An Efficient Lightweight Network for Underwater Small Object Detection

Underwater visual perception plays a crucial role in marine resource exploration, engineering inspection, and ecological monitoring. However, detecting small underwater objects remains challenging due to degraded image quality, complex backgrounds, and computational constraints in embedded systems. This paper presents a lightweight and efficient underwater small object detection framework that achieves a balance between accuracy, inference speed, and deployability. The proposed network employs a partial convolution-based lightweight backbone to reduce redundant computation, an enhanced attention mechanism integrating statistical pooling and multi-scale convolution for refined texture perception, and a multi-branch auxiliary fusion network to preserve spatial and semantic information across scales. Evaluations on the URPC2021 dataset show that the framework attains 83.3% mAP@0.5, 86.4% recall, and 103 FPS with only 3.1M parameters and 7.5 GFLOPs, outperforming existing state-of-the-art lightweight detectors. The results confirm its strong potential for real-time deployment in underwater robotic and embedded applications.