Edge-Guided Multi-Scale YOLOv11n: An Advanced Framework for Accurate Ship Detection in Remote Sensing Imagery

Ship detection in optical remote sensing imagery plays a vital role in maritime surveillance and environmental monitoring. However, existing deep learning models often struggle to generalize effectively in complex marine environments due to challenges such as noise interference, small object sizes, and diverse weather conditions. To address these issues, this study proposes an Edge-Guided Multi-Scale YOLO algorithm (YOLOv11n-EGM). The approach introduces multi-scale deep convolutional branches with varying kernel sizes to perform parallel feature extraction, enhancing the model’s ability to detect objects of different scales. Additionally, the classic Sobel operator is incorporated for edge-aware feature extraction, improving the model’s sensitivity to object boundaries. Finally, 1×1 convolutions are employed for feature fusion, reducing computational complexity. Experimental results on the ShipRSImageNet V1.0 dataset demonstrate that the improved model achieves notable gains in precision, recall, mAP@0.5, and mAP@0.5:0.95 compared to the baseline, highlighting its superior performance in challenging maritime scenarios. Qualitative analysis further shows that YOLOv11n-EGM can accurately detect both large and extremely small ships in cluttered scenes, with precise boundary localization. However, occasional misclassification in fine-grained categories (e.g., motorboat vs. hovercraft) highlights the challenge of small-instance recognition. Overall, the proposed method exhibits strong robustness and practical applicability in real-world maritime scenarios, offering a promising solution for edge-aware, multi-scale ship detection in remote sensing imagery.

@article{Shibo2025,
  title     = {Edge-Guided Multi-Scale YOLOv11n: An Advanced Framework for Accurate Ship Detection in Remote Sensing Imagery},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yan Shibo and Liu Pan and Abudhahir Buhari},
  doi       = {10.14569/IJACSA.2025.0160824},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160824}
}