EHD-YOLO: An Enhanced YOLOv8-Based Model for Halibut Detection in Complex Environments 


Vol. 14,  No. 11, pp. 880-888, Nov.  2025
https://doi.org/10.3745/TKIPS.2025.14.11.880


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  Abstract

In halibut aquaculture environments, object detection is particularly challenging due to multiple factors such as fish overlapping, optical distortions caused by water surface waves, and the occurrence of bubbles. Especially for species like halibut, which have a broad body shape and a high frequency of overlapping, conventional object detection models often exhibit limitations in accuracy. To address these problems, this paper proposes EHD-YOLO (Enhanced Halibut Detection-YOLO), an improved model that integrates LSKA (Large Separable Kernel Attention), CBAM (Convolutional Block Attention Module), and CARAFE (Content-Aware ReAssembly of FEatures) modules into YOLOv8, while modifying the feature pyramid structure into an MCFP (Multi-Convolution Focused Pyramid) to enhance detection performance. The proposed model demonstrates robust detection capability not only for overlapping halibut but also against visual noise introduced by water surface waves and bubbles. For performance evaluation, feature maps generated at different pyramid levels were utilized, and comparative experiments were conducted against baseline models such as YOLOv8n, CCS-YOLOv8, and CBR-YOLO. The results show that the EHD-YOLO P234 model, which employs lower pyramid levels, reduced the number of parameters by 62%, but its computational cost increased by 1.64 times, leading to slower inference speed and decreased detection accuracy. In contrast, the EHD-YOLO P345 model required relatively higher computation, yet achieved superior results across key performance metrics (mAP, Precision, Recall, etc.) compared to existing models. Moreover, in real-world aquaculture environments with overlapping fish, water waves, and bubbles, the EHD-YOLO P345 model effectively reduced recognition errors, demonstrating its robustness and applicability

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[IEEE Style]

S. Kang, H. K. Lim, H. S. Son, "EHD-YOLO: An Enhanced YOLOv8-Based Model for Halibut Detection in Complex Environments," The Transactions of the Korea Information Processing Society, vol. 14, no. 11, pp. 880-888, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.11.880.

[ACM Style]

Seongwang Kang, Han Kyu Lim, and Hyun Seung Son. 2025. EHD-YOLO: An Enhanced YOLOv8-Based Model for Halibut Detection in Complex Environments. The Transactions of the Korea Information Processing Society, 14, 11, (2025), 880-888. DOI: https://doi.org/10.3745/TKIPS.2025.14.11.880.

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