Lightweight Shrimp Disease Detection Research Based on YOLOv8n

TL;DR

This paper introduces a lightweight YOLOv8n-based model for shrimp disease detection, achieving high accuracy and efficiency, with significant parameter reduction and robust generalization across datasets.

Contribution

The study develops a novel lightweight network architecture with specialized modules and attention mechanisms to improve shrimp disease detection accuracy and efficiency.

Findings

32.3% reduction in parameters compared to YOLOv8n

92.7% mAP@0.5 on shrimp dataset, 3% improvement

Outperforms other lightweight models in accuracy and size

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.