Lightweight Shrimp Disease Detection Research Based on YOLOv8n

• URL: http://arxiv.org/abs/2507.02354v1
• Date: Thu, 03 Jul 2025 06:38:55 GMT
• Title: Lightweight Shrimp Disease Detection Research Based on YOLOv8n
• Authors: Fei Yuhuan, Wang Gengchen, Liu Fenghao, Zang Ran, Sun Xufei, Chang Hao,
• Abstract summary: Shrimp diseases are one of the primary causes of economic losses in shrimp aquaculture.<n>This paper proposes a lightweight network architecture based on YOLOv8n.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shrimp diseases are one of the primary causes of economic losses in shrimp aquaculture. To prevent disease transmission and enhance intelligent detection efficiency in shrimp farming, this paper proposes a lightweight network architecture based on YOLOv8n. First, by designing the RLDD detection head and C2f-EMCM module, the model reduces computational complexity while maintaining detection accuracy, improving computational efficiency. Subsequently, an improved SegNext_Attention self-attention mechanism is introduced to further enhance the model's feature extraction capability, enabling more precise identification of disease characteristics. Extensive experiments, including ablation studies and comparative evaluations, are conducted on a self-constructed shrimp disease dataset, with generalization tests extended to the URPC2020 dataset. Results demonstrate that the proposed model achieves a 32.3% reduction in parameters compared to the original YOLOv8n, with a mAP@0.5 of 92.7% (3% improvement over YOLOv8n). Additionally, the model outperforms other lightweight YOLO-series models in mAP@0.5, parameter count, and model size. Generalization experiments on the URPC2020 dataset further validate the model's robustness, showing a 4.1% increase in mAP@0.5 compared to YOLOv8n. The proposed method achieves an optimal balance between accuracy and efficiency, providing reliable technical support for intelligent disease detection in shrimp aquaculture.

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