New AI Model Reads Ship Headings From Space With Record Accuracy

Every day, satellites pass silently over the world’s busiest ports and shipping lanes, photographing thousands of vessels from hundreds of kilometers above. Buried inside those images is information that port authorities, maritime traffic managers, and search-and-rescue teams urgently need: not just where ships are, but which way each one is pointing. A new artificial intelligence system called Mamba-OrthoNet, described in the journal Complex & Intelligent Systems, now promises to extract that directional information with unprecedented reliability, reaching 96.3 percent mean average precision and 94.8 percent orientation accuracy on a demanding benchmark of high-resolution remote-sensing imagery.

The problem the researchers set out to solve is deceptively simple to state and notoriously hard to crack. Detecting a ship in a satellite image is one thing; determining its orientation, meaning the direction its bow faces relative to the image frame, is quite another. In crowded harbor scenes, vessels are moored side by side, bow to stern, sometimes nearly touching. An algorithm that looks only at the big picture, capturing the global layout of the scene, can separate adjacent ships from one another but may miss the subtle visual cues that distinguish a ship’s pointed bow from its squared-off stern. Conversely, an algorithm that zooms in on fine local details can read those bow and stern cues but loses track of the surrounding context that keeps neighboring ships from blurring together. The team behind Mamba-OrthoNet argues that neither strategy alone is sufficient, and that the key to accurate orientation detection lies in fusing the two perspectives in a principled, progressive way.

At the heart of the new architecture is an unusual pairing of two very different computational machinery. One branch of the network is a conventional convolutional neural network, or CNN, the workhorse of computer vision that excels at picking out local patterns such as edges, textures, and fine structural details. The other branch is built on a Mamba module, a member of the state-space model family that has recently attracted intense attention as an alternative to transformers. State-space models process sequences while maintaining a compact internal memory of everything they have seen, which allows them to capture long-range, global context with far less computational cost than attention-based transformers. By running these two branches in parallel, Mamba-OrthoNet effectively gives the network both a magnifying glass and a wide-angle lens at the same time.

Simply running two branches, however, does not guarantee that their outputs will combine productively. The features produced by a CNN and those produced by a state-space model live in different representational spaces, and naively adding or concatenating them can introduce redundancy and conflict. To address this, the researchers introduced a component they call Orthogonal Feature Fusion, or OFF. OFF first maps both streams of features into a shared space where they can be meaningfully compared, then decomposes the CNN-derived features to extract their detail-bearing components. By doing so, it reduces redundancy between the two streams, ensuring that the global context from the Mamba branch and the local detail from the CNN branch complement rather than duplicate each other. The result is a fusion that is compatible by construction rather than left for the network to figure out through training alone.

Fusion in Mamba-OrthoNet is also deliberately gradual rather than abrupt. The architecture integrates the two branches through a process the authors describe as moving from representation alignment to residual refinement. A component called progressive residual enhancement, or PRE, aggregates residuals layer by layer, iteratively polishing the fused features as information flows through the network. In practical terms, this means the model does not commit to a single moment of merging global and local information; instead, it keeps refining the combined representation at multiple stages, allowing coarse scene understanding and fine-grained detail to inform each other repeatedly. The team further strengthened the network’s ability to handle ships of wildly different scales, from small fishing boats to massive container vessels, by adopting a bidirectional fusion scheme known as FPBiFusion and introducing a normalization module, ConvNorm, built with a CSPRep structure to produce stronger multi-scale representations.

Perhaps the most ingenious part of the system, however, lies in how it handles angles. Predicting a ship’s heading as a single regression value sounds straightforward until one remembers that angles are circular. A ship heading at 359 degrees is pointing almost exactly the same direction as one heading at 1 degree, yet a naive regression model treats those numbers as being nearly 360 degrees apart. This periodicity, and the resulting discontinuity at the 0-degree and 360-degree boundary, is a well-known source of training instability in orientation detection, causing models to produce erratic predictions and converge poorly. Different angle encodings have been proposed over the years, but each involves trade-offs between precision and stability.

The Mamba-OrthoNet team’s answer is a scheme they call dual-granularity ring encoding, or DGRE. The idea is to represent the full 0-to-360-degree range as a ring, combining two levels of granularity. At the coarse level, the ring is divided into cyclic bins, giving the model a stable, wrap-around-aware sense of roughly where an angle lies. At the fine level, smoothed sub-bins sharpen the estimate, allowing the model to distinguish headings that differ by only a few degrees. Because the coarse structure already respects the circular nature of angles, the fine refinement never has to fight the 0-degree and 360-degree discontinuity, and the model can produce stable predictions across the entire angular range. Ablation studies reported in the paper confirm that DGRE, together with OFF and PRE, contributes measurably to the system’s overall performance under the benchmark setting.

The benchmark in question, DOTA-ORShip, is a dataset of oriented ship annotations in high-resolution remote-sensing images, a setting that captures exactly the crowded, multi-scale conditions of real ports. On this dataset, Mamba-OrthoNet achieved 96.3 percent mean average precision at the standard 50 percent overlap threshold, along with 94.8 percent orientation accuracy. Those figures place the system among the strongest reported results for ship orientation detection, and the ablation experiments, in which individual components were removed to measure their contribution, support the claim that the architecture’s gains come from the deliberate interplay of its parts rather than from raw capacity alone.

The practical implications extend well beyond a leaderboard number. Accurate ship orientation is a foundational input for maritime domain awareness. Traffic management systems use vessel heading to predict trajectories and prevent collisions in congested straits and anchorages. Search-and-rescue operations rely on knowing which way drifting or maneuvering vessels are pointed to model drift and plan interception routes. Port operators use orientation data to verify berthing and monitor mooring compliance. A detector that confuses bow with stern, or that destabilizes when a ship’s heading crosses the 0-degree line, can propagate errors into all of these downstream systems. By combining global context, local detail, and circularly consistent angle encoding in a single network, Mamba-OrthoNet aims to remove several of the most persistent failure modes at once.

The work also reflects a broader shift in the deep learning landscape. State-space models like Mamba have moved rapidly from language modeling experiments into computer vision, offering a way to capture long-range dependencies with linear computational scaling, in contrast to the quadratic cost of transformer attention. The Chengdu-based research team’s demonstration that a Mamba branch can be fused productively with a CNN branch, through orthogonal feature mapping and progressive residual refinement, offers a template that may generalize beyond ships to other oriented-object detection problems in aerial imagery, from vehicles on highways to aircraft on tarmacs. The article, published open access under a Creative Commons Attribution license, was authored by Jiandan Zhong, Lingfeng Liu, Tao Yu, Zhipeng Yang, Yingxiang Li, Yajuan Xue, and Fei Song, with affiliations spanning Chengdu University of Information Technology, Xizang University, and Xihua University. As satellites multiply and image resolutions improve, the volume of remote-sensing data demanding automated interpretation will only grow, and architectures like this one suggest that the tools for reading that data, down to the direction every vessel points, are becoming both sharper and more dependable.

Subject of Research: Deep learning-based ship orientation detection in high-resolution remote-sensing imagery

Article Title: Mamba-OrthoNet: unified global–local feature fusion and angular encoding for accurate ship orientation detection

Article References: Mamba-OrthoNet: unified global–local feature fusion and angular encoding for accurate ship orientation detection. (n.d.). https://doi.org/10.1007/s40747-026-02518-7

Image Credits: AI Generated

DOI: 10.1007/s40747-026-02518-7

Keywords: Mamba-OrthoNet, ship orientation detection, remote sensing, state-space models, Mamba, convolutional neural networks, feature fusion, angle encoding, object detection, DOTA-ORShip, maritime monitoring, computer vision