# Maritime Small Target Image Detection Algorithm Based on Improved YOLOv11n

## TL;DR

This paper introduces an improved YOLOv11n algorithm for detecting small ships in complex maritime environments using infrared and visible light images.

## Contribution

The novel contribution is the integration of BIE, C3k2-RepViTBlock, and ConvAttn modules to enhance small target detection in maritime settings.

## Key findings

- The improved algorithm increased mAP@0.5 by 1.9% and 1.7% on two datasets.
- Average precision improved by 2.2% and 2.4% compared to the original model.
- The model maintains lightweight design while reducing missed detections.

## Abstract

Aiming at the problems of small-sized ships (such as small patrol boats) in complex open-sea backgrounds, including small sizes, insufficient feature information, and high missed detection rates, this paper proposes a maritime small target image detection algorithm based on the improved YOLOv11n. Firstly, the BIE module is introduced into the neck feature fusion stage of YOLOv11n. Utilizing its dual-branch information interaction design, independent branches for key features of maritime small targets in infrared and visible light images are constructed, enabling the progressive fusion of infrared and visible light target features. Secondly, RepViTBlock is incorporated into the backbone network and combined with the C3k2 module of YOLOv11n to form C3k2-RepViTBlock. Through the lightweight attention mechanism and multi-branch convolution structure, this addresses the insufficient capture of tiny target features by the C3k2 module and enhances the model’s ability to extract local features of maritime small targets. Finally, the ConvAttn module is embedded at the end of the backbone network. With its dynamic small-kernel convolution, it adaptively extracts the contour features of small targets, maintaining the overall model’s light weight while reducing the missed detection rate for maritime small targets. Experiments on a collected infrared and visible light ship image dataset (IVships) and a public dataset (SeaShips) show that, on the basis of increasing only a small number of parameters, the improved algorithm increases the mAP@0.5 by 1.9% and 1.7%, respectively, and the average precision by 2.2% and 2.4%, respectively, compared with the original model, which significantly improves the model’s small target detection capabilities.