A spatio-temporal attention enhanced CNN method for marker localization in AUV docking

Underwater docking of autonomous underwater vehicles (AUVs) was typically dependent on the complete visual detection of markers. When markers were only partially visible due to occlusion or departure from the field of view, conventional localization methods based on complete features were rendered ineffective, resulting in the interruption of docking operations. To address this limitation, an enhanced orientation-aware method based on a spatiotemporal attention convolutional neural network (CNN) was proposed in this study. The core of this method was a dual-path feature fusion architecture: discriminative features of visible marker segments were extracted from single frames by the spatial path, while the temporal path was employed to aggregate features across consecutive frames, thereby compensating for the insufficiency of single-frame information. These two pathways were adaptively fused through a spatiotemporal attention module, which was designed to dynamically focus on the most informative cues. Consequently, robust qualitative judgment of the marker’s relative orientation was achieved. Experimental validation conducted in underwater environments demonstrated that stable orientation awareness was maintained by the proposed method even under conditions where the marker was severely off-center or largely obscured. This approach was shown to significantly extend the initial capture range for AUV docking guidance, and the robustness and operational continuity of the system under extreme visual conditions were effectively enhanced.