FPH-DEIM: A Lightweight Underwater Biological Object Detection Algorithm Based on Improved DEIM

Underwater biological object detection plays a critical role in intelligent ocean monitoring and underwater robotic perception systems. However, challenges such as image blurring, complex lighting conditions, and significant variations in object scale severely limit the performance of mainstream detection algorithms like the YOLO series and Transformer-based models. Although these methods offer real-time inference, they often suffer from unstable accuracy, slow convergence, and insufficient small object detection in underwater environments. To address these challenges, we propose FPH-DEIM, a lightweight underwater object detection algorithm based on an improved DEIM framework. It integrates three tailored modules for perception enhancement and efficiency optimization: a Fine-grained Channel Attention (FCA) mechanism that dynamically balances global and local channel responses to suppress background noise and enhance target features; a Partial Convolution (PConv) operator that reduces redundant computation while maintaining semantic fidelity; and a Haar Wavelet Downsampling (HWDown) module that preserves high-frequency spatial information critical for detecting small underwater organisms. Extensive experiments on the URPC 2021 dataset show that FPH-DEIM achieves a mAP@0.5 of 89.4%, outperforming DEIM (86.2%), YOLOv5-n (86.1%), YOLOv8-n (86.2%), and YOLOv10-n (84.6%) by 3.2–4.8 percentage points. Furthermore, FPH-DEIM significantly reduces the number of model parameters to 7.2 M and the computational complexity to 7.1 GFLOPs, offering reductions of over 13% in parameters and 5% in FLOPs compared to DEIM, and outperforming YOLO models by margins exceeding 2 M parameters and 14.5 GFLOPs in some cases. These results demonstrate that FPH-DEIM achieves an excellent balance between detection accuracy and lightweight deployment, making it well-suited for practical use in real-world underwater environments.