Infrared Ship Detection Based on Improved YOLO-11

In the marine field, infrared detection technology is of great significance for timely localization and detection of ships in security missions. However, since infrared ship targets are often in the environmental conditions of small pixel occupancy, low contrast and complex background, it poses a great challenge for multiship detection, classification, and localization tasks. Therefore, to solve these problems, we propose a lightweight enhanced small target infrared ship target detection (YOLO-FFE) network, Designed lightweight and efficient C2FFA module to increase the feature extraction capability of the backbone network. A lightweight attention mechanism, ELA, is added to the small-target detection header to better learn and predict the locations and bounding boxes of small targets in ship images. Experiments demonstrate that the proposed YOLO-FFE has better detection performance for infrared ship targets.

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Publication Details

Published
2024-12-22
DOI
https://doi.org/10.1109/cicn63059.2024.10847489
Citations
2
Primary Topic
Infrared Target Detection Methodologies
Type
article
Field-Weighted Citation Impact
2.60
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article

Infrared Ship Detection Based on Improved YOLO-11

Yuhao Hu
2 citations
Infrared Target Detection Methodologies
2.60
article

Infrared Ship Detection Based on Improved YOLO-11

Yuhao Hu
article en
2 citations

Abstract

In the marine field, infrared detection technology is of great significance for timely localization and detection of ships in security missions. However, since infrared ship targets are often in the environmental conditions of small pixel occupancy, low contrast and complex background, it poses a great challenge for multiship detection, classification, and localization tasks. Therefore, to solve these problems, we propose a lightweight enhanced small target infrared ship target detection (YOLO-FFE) network, Designed lightweight and efficient C2FFA module to increase the feature extraction capability of the backbone network. A lightweight attention mechanism, ELA, is added to the small-target detection header to better learn and predict the locations and bounding boxes of small targets in ship images. Experiments demonstrate that the proposed YOLO-FFE has better detection performance for infrared ship targets.

Northeast Electric Power University (CN)
Affordable and clean energy
Openalex Percentile: Top 7%
Infrared Target Detection Methodologies
2.60
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