Instance segmentation of ships plays a crucial role in tasks such as monitoring, identification, and tracking, thereby supporting intelligent navigation. However, the wide variability in ship shapes and scales, coupled with environmental interference, leads to poor performance of existing methods perform in Ship Contour extraction. To address this issue, this paper proposes a novel Ship Contour method based on curve recursion.

By enhancing CenterNet with hierarchical feature extraction and integrating the Deep Layer Aggregation-60 backbone network, the proposed method achieves a balance between accuracy and speed. The Block structure is optimized, and an ECA channel attention mechanism is incorporated to strengthen feature extraction, while the Mish activation function replaces ReLU to improve adaptability in deep learning. In addition, a translation-invariant contour deformation method and a dynamic matching loss function are introduced to accelerate the final contour extraction.

On the dedicated 2023Ship-seg dataset containing 2300 samples, the proposed method achieved an average precision of AP_0.5∶0.95 = 64.0% and a recall rate of AR_0.5∶0.95 = 67.9%, outperforming all mainstream instance segmentation algorithms.

The method can significantly improve visual processing performance in ship monitoring and intelligent navigation scenarios.

Current ship navigation decision-making systems struggle to demonstrate superior performance in undefined sailing scenarios. Given the broad applicability of large language models （LLMs） in unknown scenarios, this study proposes a dual-LLM-core-driven adaptive ship navigation agent architecture （Nav-DLLC） to address this issue.

Nav-DLLC employs ReAct-based prompting to decompose complex navigation tasks into manageable subtasks and invoke external tools for information collection, reducing LLM errors. Subsequently, a small-parameter LLM fine-tuned with low-rank adaptation （LoRA） serves as the collision avoidance core, processing unstructured data to generate COLREG-compliant decisions.

Simulation experiments show that Nav-DLLC achieves outstanding performance in both traditional ship collision avoidance tasks and unstructured dynamic scenarios. Its collision avoidance accuracy is 86%, and its behavior compliance rate is 90%, significantly outperforming LLM baselines and traditional methods such as the Dynamic Window Approach （DWA） and Artificial Potential Field （APF）. The decision core's single-decision latency is 11.13 seconds, higher than the 0.73 seconds of traditional methods, yet still within the safe time window for collision avoidance decision-making.

Nav-DLLC bridges the gap between traditional navigation systems and LLM technology, providing a safe and efficient intelligent decision-making paradigm for complex navigation environments and promoting the intelligent development of ship navigation.

To meet the requirements of remotely controlling ship in curved, narrow and crowded inland waterways, this paper proposes an approach that consists of CNN-based algorithms and knowledge based models under ship-shore cooperation conditions.

On the basis of analyzing the characteristics of ship-shore cooperation, the proposed approach realizes autonomous perception of the environment with visual simulation at the core and navigation decision-making control based on deep reinforcement learning, and finally constructs an artificial intelligence system composed of image deep learning processing, navigation situation cognition, route steady-state control and other functions. Remote control and short-time autonomous navigation of operating ships are realized under inland waterway conditions, and remote control of container ships and ferries is carried out.

The proposed approach is capable of replacing manual work by remote orders or independent decision-making, as well as realizing independent obstacle avoidance, with a consistent deviation of less than 20 meters.

The developed prototype system carries out the remote control operation demonstration of the above ship types in such waterways as the Changhu Canal Shenzhou line and the Yangtze River, proving that a complete set of algorithms with a CNN and reinforcement learning at the core can independently extract key navigation information, construct obstacle avoidance and control awareness, and lay the foundation for inland river intelligent navigation systems.