收稿：2025-08-23，
纸质出版：2026-07-07
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卫星遥感图像舰船目标检测与识别在海洋监视、航运管理和军事侦察等领域具有重要应用价值。然而，现有数据集在规模、多样性和细粒度方面的不足严重制约了该领域的发展。为突破这一瓶颈，本文构建了大规模细粒度舰船实例检测LAFI（large-scale fine-grained ship instance detection）数据集，并基于稳定扩散模型进一步构建了一个百万级舰船目标检测数据集LAFI-Diffusion。该研究具有以下创新性贡献：（1）LAFI是目前规模最大、类别最丰富的公开舰船数据集，包含8000张高分辨率遥感图像、49种舰船类型以及48717个精细标注实例；（2）采用可控扩散模型，在船舶—海洋文本提示引导下，可在不同的海况、天气条件和时间变化下生成数百万幅合成图像，支撑目标检测模型的预训练，显著提升模型在复杂海洋场景下的泛化能力，同时减少环境干扰；（3）基于LAFI数据集，本文系统评估了当前主流的7种有向框检测算法，为后续研究提供了重要基准。
Fine-grained ship detection in optical remote sensing imagery is an important research topic in maritime monitoring and ocean observation. Compared with coarse-grained ship detection
fine-grained ship detection aims to distinguish specific ship types with highly similar visual appearances
which places higher demands on dataset scale
category diversity
and annotation accuracy. However
existing ship detection datasets are generally limited in the number of fine-grained categories
instance scale
and scene diversity
which restricts the performance and generalization ability of deep learning-based oriented object detection models. The objective of this study is to construct a large-scale fine-grained ship detection dataset and expand data diversity through controllable synthetic data generation
thereby providing a reliable data foundation and benchmark for fine-grained ship detection in optical remote sensing imagery.
First
a large-scale fine-grained ship detection dataset
named LAFI
is constructed using high-resolution optical remote sensing images collected from 36 representative ports worldwide. The dataset contains 8
000 images acquired under diverse imaging conditions and complex maritime environments. A total of 49 fine-grained ship categories are defined
and 48
717 ship instances are manually annotated using oriented bounding boxes to accurately describe ship orientation and geometric characteristics. Second
to alleviate the limitations of real data in terms of scale and scene coverage
this study designs a controllable diffusion-based data generation framework to extend LAFI into a million-scale synthetic dataset
referred to as LAFI-Diffusion. The diffusion model is guided by incorporating structured textual prompts that describe scene types
weather conditions
and temporal information to generate realistic ship images under diverse maritime scenarios. The generated synthetic samples are further filtered and combined with real data to form large-scale training sets suitable for fine-grained ship detection. Finally
several representative oriented object detection methods are selected and evaluated on the constructed datasets to analyze the effectiveness of synthetic data augmentation and establish benchmark results.
Experimental results show that the proposed LAFI dataset provides improved category richness
instance scale
and scene diversity compared with existing fine-grained ship detection datasets. Incorporating synthetic data from LAFI-Diffusion into the training process consistently enhances detection performance and generalization ability across different oriented object detection models. Performance gains are particularly evident in complex maritime environments
such as crowded ports and scenes with varying sea states and illumination conditions. Benchmark evaluations indicate that the contribution of synthetic data varies across detection methods
suggesting that appropriate integration strategies are important for fully exploiting synthetic data.
This paper presents a large-scale fine-grained ship detection dataset that integrates real optical remote sensing imagery with controllable diffusion-based synthetic data generation. By substantially expanding data scale and enhancing scene diversity
the proposed LAFI-Diffusion dataset effectively addresses the limitations of existing fine-grained ship detection benchmarks. The experimental results confirm that synthetic data can serve as an effective complement to real-world samples
improving detection accuracy and robustness for oriented ship detection models. The released datasets and benchmark results provide valuable support for future research on fine-grained ship detection and related remote sensing applications.
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