Underwater Seafood Detection Using Deep Learning and Data Augmentation

Abstract

Accurate seafood detection underwater is still a real challenge for modern fisheries, especially when it comes to resource monitoring or automated harvesting. In real-world conditions, underwater images are often blurry, discolored, or cluttered with background noise, all of which make feature extraction harder. On top of that,many marine species have irregular, elongated body shapes, which adds another layer of difficulty for reliable detection. To tackle these problems, we developed an underwater seafood detection model called Seafood Detection Deep Learning (SDDL). It builds on the YOLOv8 framework but integrates dynamic snake convolution to better handle elongated features like sea urchin spines or sea cucumber tentacles. This change lets the network deal more effectively with anisotropic shapes.Before training, we apply Contrast Limited Adaptive Histogram Equalization to boost image contrast and bring out finer details. We also use Focal Loss during training to reduce the impact of class imbalance. The dataset we used contains 5543 underwater images, which provides a realistic and fairly challenging testbed. SDDL achieves 84.22% mAP50 and 48.74% mAP50–95 on this dataset. When we compared it against several representative methods, SDDL gave more consistent results under difficult imaging conditions. Overall,these findings suggest the model holds good promise for deployment in underwater robotic systems, helping enable accurate seafood detection and supporting intelligent, automated harvesting in fisheries.

Received: 28 September 2025 | Revised: 27 March 2026 | Accepted: 24 April 2026

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The URPC dataset that support the findings of this study is available in Github at https://github.com/mousecpn/DG-YOLO.

Author Contribution Statement

Hanyu Jiang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration, Funding acquisition. Zhichao Zheng: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review &editing, Supervision.