Underwater Robot Object Detection Algorithm Based on YOLOv11

Despite the ocean's vast energy reserves and extensive coverage of Earth's surface, the complexity of the underwater environment has hindered effective target recognition. To mitigate the feature degradation caused by underwater scattering, wavelength-dependent absorption, and non-uniform illumination, this study proposes a degradation-aware YOLOv11s-based detection framework for underwater robotic object detection. The framework enhances detection robustness by improving feature reconstruction, channel-spatial attention, and bounding-box regression in a unified architecture. First, to address limitations in model parameters, spatial channel reconstruction convolutions (SCConv) replace certain traditional convolutions. Sequential reconstruction via SRU-CRU effectively suppresses spatial and channel redundancy, enabling a more precise capture of underwater target deformations and complex features. Second, the Shuffle Attention module enhances the interaction between channel and spatial features, improving the model's fine-grained representation of underwater targets, highlighting granular objects and key textures. Finally, Focaler-IoU is employed to linearly remap the IoU interval, improving the accuracy and convergence stability of bounding-box regression. These components work together to improve the model's robustness in degraded underwater scenes. In the underwater robotic detection task, the improved model achieves an mAP@0.5 of 88.4%, which is 3.2 percentage points higher than that of the baseline YOLOv11s. These results indicate that the proposed model improves detection accuracy while maintaining the real-time requirements of underwater robotic applications.