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An improved anchor-free SAR ship detection algorithm based on brain-inspired attention mechanism.
Hao Shi1,2,3, Cheng He1,3, Jianhao Li1,3
1Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing, China.
Frontiers in Neuroscience
|December 19, 2022
Summary
This study introduces an improved anchor-free algorithm for synthetic aperture radar (SAR) ship detection, utilizing a brain-inspired attention mechanism to enhance accuracy and robustness in complex backgrounds. The novel approach improves feature extraction and noise suppression for better ship identification.
Area of Science:
- Computer Science
- Artificial Intelligence
- Remote Sensing
Background:
- Synthetic Aperture Radar (SAR) ship detection is crucial for military and civilian applications but faces challenges with varying ship scales and feature differences.
- Existing anchor-based methods struggle with generalization and performance in multi-scale SAR ship detection.
- SAR image speckle noise can be mistaken for ship edges, impacting localization accuracy.
Purpose of the Study:
- To propose an improved anchor-free SAR ship detection algorithm incorporating a brain-inspired attention mechanism.
- To enhance the robustness and accuracy of SAR ship detection, particularly for multi-scale targets and complex backgrounds.
- To address the challenge of speckle noise interference in SAR ship localization.
Main Methods:
- An anchor-free detection network is employed to directly identify potential target locations, improving robustness over traditional anchor-based methods.
- A dense connection module is integrated for deep feature fusion, enhancing feature extraction capabilities.
- A visual attention module is introduced in the shallow network layers to focus on salient ship features and suppress background noise.
- A novel width-height prediction constraint is proposed to mitigate the impact of speckle noise on localization accuracy.
Main Results:
- The proposed algorithm demonstrates superior performance on the SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Images Dataset (HRSID).
- The algorithm achieved an Average Precision (AP) of 68.2% on SSDD and 62.2% on HRSID.
- Experimental results validate the effectiveness of the anchor-free approach, dense connections, visual attention, and width-height prediction constraint.
Conclusions:
- The brain-inspired attention mechanism significantly improves SAR ship detection by focusing on target features and reducing background interference.
- The anchor-free strategy combined with enhanced feature fusion and noise suppression leads to more robust and accurate ship detection in SAR images.
- This algorithm offers a promising solution for challenging SAR ship detection tasks, achieving state-of-the-art performance.
