ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Ocean Observation
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1679077
This article is part of the Research TopicIntegrating Unmanned Platforms and Deep Learning Technologies for Enhanced Ocean Observation and Risk Mitigation in Ocean EngineeringView all 4 articles
RCDI-YOLO: A Target-Detection Method for Complex Environment Side-Scan Sonar Images Based on Improved YOLOv8
Provisionally accepted
Jiaoyang Zhang*
Bo Gao- The Key Laboratory of Mariculture, Ocean University of China, Ministry of Education, Qingdao, China
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Target detection in side-scan sonar images under complex environments is challenging due to noisy backgrounds, occlusion, and blurred target boundaries, which reduce the accuracy and robustness of traditional methods. To address these issues, we propose RCDI-YOLO, an enhanced YOLOv8-based detection framework that integrates rotation-aware feature extraction, multi-scale feature integration, and implicit feature representations for noise suppression. In addition, a diversified complex environment side-scan sonar dataset (CESSSD) is constructed to mitigate data scarcity and imbalance. Experimental results demonstrate that RCDI-YOLO achieves a detection accuracy of 95.3% and a mean Average Precision of 95.7%, outperforming the original YOLOv8 by 2.5% and 2.0%, respectively. These findings confirm that RCDI-YOLO significantly improves detection performance in complex underwater environments, particularly in scenarios with occlusion, cluttered backgrounds, and noise interference, highlighting its potential for underwater detection and search-and-rescue applications.
Keywords: Side-scan sonar images, complex underwater environments, Data augmentation, target detection, YOLOv8
Received: 04 Aug 2025; Accepted: 10 Oct 2025.
Copyright: © 2025 Zhang and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jiaoyang Zhang, 13340998176@163.com
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