Intelligent ship navigation has become a core technological enabler for accelerating the digital, intelligent and low-carbon transformation of the global shipping industry. Driven by the International Maritime Organization's greenhouse gas emission reduction targets and China's strategic policies for intelligent shipping development, ship intelligent navigation systems are evolving from experimental validation toward engineering applications. However, several key challenges remain in practical implementation. In particular, the coordination mechanism among navigation decision-making, propulsion response, and energy-efficiency constraints lacks a unified architectural representation. In addition, the adaptive mapping logic between shipboard intelligent capability levels and shore-based hierarchical operation modes has not yet been systematically established. To address these gaps, this study focuses on the architectural design of a bridge–engine integrated ship intelligent navigation system and investigates the adaptive mechanisms of ship–shore collaborative operation.

In terms of research methods, this paper firstly defines the functional connotation, system boundaries, and essential characteristics of bridge–engine integration based on existing regulations for intelligent ships and relevant domestic and international research outcomes. Secondly, a hierarchical intelligent navigation architecture is established. A three-stage evolution pathway for shipboard autonomy is proposed, including enhanced navigation, assisted navigation and autonomous navigation. In parallel, three shore-based operation modes are defined, namely monitoring, remote control, and supervisory navigation. On this basis, an asymmetric ship–shore functional adaptation matrix is developed to systematically clarify recommended combinations, restricted feasible combinations, and inapplicable combinations, with typical application scenarios clearly specified. Finally, key enabling technologies are comprehensively analyzed, including environmental perception and situational awareness, navigation decision-making and path planning control, intelligent engine room operation and energy efficiency management, system testing and evaluation, as well as ship–shore human-machine collaborative control.

The research results systematically present the overall hierarchical architecture of a bridge–engine integrated ship intelligent navigation system under ship–shore collaboration. They clarify the functional connotation and operational boundaries of each shipboard capability level and shore-based operation mode, and reveal the asymmetric adaptation rules and principles of control authority allocation between shipboard and shore-end systems.

It is concluded that the proposed system architecture and asymmetric adaptation matrix can provide a solid theoretical foundation and technical reference for the engineering implementation of intelligent ship navigation systems and the development of relevant industrial standards. Furthermore, the findings offer important theoretical guidance for improving ship–shore collaborative operation mechanisms, standardizing control authority switching, and promoting the development and application of a new-generation intelligent and green maritime transportation system.

It is vital to detect and track ships during coastal monitoring and ship navigation over long distances in complex circumstances. However, due to their small size and unclear features, they can be readily confused with shorelines, noise, and rocks, making them sometimes difficult to spot immediately. To address this issue, a novel ship detection method called ShipDet is proposed which significantly improves performance through the design of a dedicated backbone network, improved feature extraction process, and constrained microscopic detection heads.

First, this method constructs a feature fusion and extraction network that is highly sensitive to small objects by integrating the Swin Transformer module (STR) with the classic CSPDarknet53 network. This enhances the correlation between small target features and the environment, establishing associations between ships and waterways, ships and other ships, and ships and coastlines, while suppressing irrelevant information. Subsequently, considering the uneven distribution and minor scale variations of ship targets in the dataset, two detection layers are retained to reduce model parameters and further enhance model performance. Moreover, the method employs the SCYLLA-IoU (SIoU) loss function to constrain the detection heads, thereby reducing regression freedom and improving detection accuracy and robustness.

To validate the proposed method, a dataset called 2023ships is established which consists of up to 9 000 samples covering various scenarios such as inland rivers, coastal areas, daytime, nighttime, and foggy weather. During testing, the proposed method demonstrates superior overall ship detection performance compared to other algorithms, with a mAP of 92.9%, a precision rate of 92.1%, and a parameter size of 35 366 310.

The proposed method can greatly benefit the fields of maritime monitoring and intelligent 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.