Scallop Segmentation Using Aquatic Images with Deep Learning Applied to Aquaculture

Nina W.; Quispe N.L.; Bernedo-Flores L.S.; García M.S.; Valdivia C.; Huanca E. · International Journal of Advanced Computer Science and Applications (2025) · DOI: 10.14569/ijacsa.2025.0160219

Resumen

This study evaluates the performance of deep learning-based segmentation models applied to underwater images for scallop aquaculture in Sechura Bay, Peru. Four models were analyzed: SUIM-Net, YOLOv8, DETECTRON2, and CenterMask2. These models were trained and tested using two custom datasets: SEG SDS GOPRO and SEG SDS SF, which represent diverse underwater scenarios, including clear and turbid waters, varying current intensities, and sandy substrates. The primary aim was to automate scallop identification and segmentation to improve the efficiency and safety of aquaculture monitoring. The evaluation showed that SUIM-Net achieved the highest accuracy of 93% and 94% on the SEG SDS GOPRO and SEG SDS SF datasets, respectively. CenterMask2 performed best on the SEG SDS SF dataset, with an accuracy of 96.5%. Additionally, a combined dataset was used, where YOLOv8 achieved an accuracy of 88%, demonstrating its robustness across varied conditions. Beyond scallop segmentation, the models were extended to detect six additional marine classes, achieving a maximum accuracy of 39.90% with YOLOv8. This research underscores the potential of deep learning techniques to revolutionize aquaculture by reducing operational risks, minimizing costs, and enhancing monitoring accuracy. The findings contribute valuable insights into the challenges and opportunities of applying artificial intelligence in underwater environments. © (2025), (Science and Information Organization). All rights reserved.

Instituciones: Universidad Católica San Pablo