MSF-DETR: A small target detection algorithm for sonar images based on spatial-frequency domain collaborative feature fusion
Heng Zhao, Shuping Han, Jiaying Geng, Yubo Han, Shuyang Jia, Ke Li, Xuebo Zhang, Xuebo Zhang, Xuebo Zhang
TL;DR
This paper introduces MSF-DETR, a new algorithm for detecting small targets in sonar images, improving accuracy and efficiency in underwater detection.
Contribution
MSF-DETR introduces a novel end-to-end detection algorithm with spatial-frequency domain fusion for small target detection in sonar images.
Findings
MSF-DETR achieves 78.5% mAP50 on the SSST-3K dataset, outperforming baseline RT-DETR by 2.8%.
The algorithm reduces computational complexity by 12.0% and reaches 71.2 FPS inference speed.
It demonstrates 38.5% mAP50-95 on the SSST-3K dataset, a 3.3% improvement over RT-DETR.
Abstract
Side-scan sonar imaging is essential for underwater target detection in marine exploration and engineering applications, yet small target detection faces significant challenges including limited frequency domain feature utilization, insufficient multi-scale feature fusion, and high computational complexity. This study develops Multi-Scale Spatial-Frequency Collaborative Detection Transformer (MSF-DETR), a novel end-to-end automatic detection algorithm specifically designed for small targets in side-scan sonar images. The method integrates three core innovations: a Multi-domain Adaptive Spatial-frequency Network (MASNet) backbone employing Cascaded dual-domain Mamba-enhanced Spatial-frequency Synergistic Convolution that simultaneously captures spatial geometric and frequency domain texture features; a Hierarchical Multi-scale Adaptive Feature Pyramid Network implementing intelligent…
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Taxonomy
TopicsUnderwater Acoustics Research · Advanced Neural Network Applications · Image Enhancement Techniques