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Performance evaluation of YOLO models for target detection from ocean sidescan sonar imagery
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DOI：10.3389/fmars.2026.1883695.png)
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Abstract
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Sidescan sonar images suffer from strong seabed clutter; low gray contrast of underwater targets and frequent missed small targets. Current detection models struggle to balance detection accuracy; inference speed and embedded deployment. To solve these problems; this paper selects six mainstream one-stage detection models; YOLOv4; YOLOv6; YOLOv7; YOLOv9; YOLOv13n and YOLO26n; for comparative experiments. Two sidescan sonar datasets D1 and D2 with distinct imaging features are built. Seven metrics are used to quantitatively evaluate overall model performance: Precision; Recall; mAP@0.5; mAP50–95; FPS; FLOPs and parameter count. Experimental results show that imaging quality directly affects detection performance. The traditional heavy model YOLOv4 has numerous parameters and high computational cost. It produces many missed and false detections under heavy noise and fails to support real-time detection on underwater equipment. Lightweight YOLOv13n and YOLO26n show stronger anti-interference ability on noisy sonar images. Among all models; YOLOv6 on D1 achieves the highest overall mAP@0.5 of 0.944; YOLOv9 on D2 delivers the best localization accuracy across multiple IoU thresholds; and YOLO26n reaches an ultra-high inference speed of 526.32 FPS with only 2.375M parameters and 5.2G FLOPs. By comparing performance gaps between the two datasets; we find that parameter size is positively correlated with overfitting risk. Large models easily memorize unique noise textures from training data and have weak generalization. Restricted by limited parameter capacity; lightweight networks naturally suppress overfitting and achieve more stable detection results across different sea conditions. This paper clarify application scenarios for each model based on accuracy; real-time performance; lightweight degree and generalization. YOLOv4 is only suitable for offline analysis in laboratories. YOLOv9 fits high-precision contour mapping tasks such as underwater archaeology. YOLOv13n works for large-area scanning with medium and small underwater vehicles. YOLO26n has the best overall engineering performance. It can be deployed on low-computing devices including portable sonars and miniature underwater vehicles for long-term continuous real-time underwater detection. This study clarifies the performance trade-offs of various YOLO models for underwater target detection on side-scan sonar images. It provides references for model selection of underwater detection systems with different hardware and task requirements. Future work will expand sonar datasets covering multiple sea areas and adopt noise augmentation strategies to further boost generalization and small target detection performance in complex deep-sea environments.
Keywords:
YOLO
underwater target detection
overfitting
small-sample learning
sidescan sonar
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