Journal Article

AquaYOLO: Enhancing YOLOv8 for Accurate Underwater Object Detection for Sonar Images

Zhang, Jingjing, Xu, Chengjun, Irfan, Muhammad, Chen, Zhe, Chen, Qinglang, Lu, Yanyang

2025

Overview

Object detection in underwater environments presents significant challenges due to the inherent limitations of sonar imaging, such as noise, low resolution, lack of texture, and color information. This paper introduces AquaYOLO, an enhanced YOLOv8 version specifically designed to improve object detection accuracy in underwater sonar images. AquaYOLO replaces traditional convolutional layers with a residual block in the backbone network to enhance feature extraction. In addition, we introduce Dynamic Selection Aggregation Module (DSAM) and Context-Aware Feature Selection (CAFS) in the neck network. These modifications allow AquaYOLO to capture intricate details better and reduce feature redundancy, leading to improved performance in underwater object detection tasks. The model is evaluated on two standard underwater sonar datasets, UATD and Marine Debris, demonstrating superior accuracy and robustness compared to baseline models.

• Publisher: MDPI AG
• Subject: Accuracy / Acoustics / Aggregation / Benchmarks / Datasets / Deep learning / Feature extraction / Feature selection / Image processing / Image resolution / marine classification / Marine debris / marine detection / Object recognition / Redundancy / Sensors / Sonar / Telecommunication systems / Telematics / tracking / Underwater / underwater classification / underwater object detection / underwater sonar images