Work

RCF-YOLOv8: A Multi-Scale Attention and Adaptive Feature Fusion Method for Object Detection in Forward-Looking Sonar Images

Year: 2025

Type: article

Abstract: Acoustic imaging systems are essential for underwater target recognition and localization, but forward-looking sonar (FLS) imagery faces challenges due to seabed variability, resulting in low resolution, blurred images, and sparse targets. To address these issues, we introduce RCF-YOLOv8, an enhanced detection framework based on YOLOv8, designed to improve FLS image analysis. Key innovations include the use of CoordConv modules to better encode spatial information, improving feature extraction and reducing misdetection rates. Additionally, an efficient multi-scale attention (EMA) mechanism addresses sparse target distributions, optimizing feature fusion and improving the network’s ability to identify key areas. Lastly, the C2f module with high-quality feature fusion (C2f-Fusion) optimizes feature extraction from noisy backgrounds. RCF-YOLOv8 achieved a 98.8% mAP@50 and a 67.6% mAP@50-95 on the URPC2021 dataset, outperforming baseline models with a 2.4% increase in single-threshold accuracy and a 10.4% increase in multi-threshold precision, demonstrating its robustness for underwater detection.

Source: Remote Sensing

DOI: 10.3390/rs17193288

Authors: Xiaoxue Li, Yuhan Chen, Xueqin Liu, Zhiliang Qin, Jiaxin Wan, Qingyun Yan

Institutions: Harbin Engineering University, Ministry of Industry and Information Technology, Nanjing University of Information Science and Technology

Language: English

Topic: Underwater Acoustics Research

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funders: National Natural Science Foundation of China, Natural Science Foundation of Shandong Province