YOLO-TPS:用于复杂水产养殖环境的多模块协同高精度鱼类疾病检测模型
Cheng Ouyang1, Hao Peng1, Mingyu Tan1
1College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.
Animals : an open access journal from MDPI
|August 28, 2025
概括
一个新的AI模型,YOLO-TPS,准确地检测鱼类疾病,提高水产养殖的可持续性和粮食安全. 这种先进的系统有助于提前发现病变,从而更好地监测鱼类的健康状况.
科学领域:
- 水产养殖
- 动物健康
- 计算机视觉
背景情况:
- 水产养殖对全球粮食安全至关重要,但受到鱼类疾病的威胁.
- 手动诊断疾病是低效和不准确的,阻碍了可持续的水产养殖.
- 在复杂的环境中检测小,多样化的病变是具有挑战性的.
研究的目的:
- 开发用于水产养殖的高精度鱼类疾病检测模型 (YOLO-TPS).
- 解决小损伤检测,大小变化和背景复杂性的局限性.
- 提高鱼类疾病诊断的准确性和效率.
主要方法:
- 使用了改进的YOLOv11n架构.
- 整合了多模块协同效应战略和三重关注机制.
- 集成了SPPF_TSFA和PC_Shuffleblock模块,以及一个可识别规模的动态 IoU 损失函数 (SDIoU).
主要成果:
- YOLO-TPS模型实现了卓越的性能:mAP0.5: 97.2%,精度:97.9%,回忆:95.1%.
- 展示了增强的多尺度特征提取和空间意识.
- 在检测六个类别的鱼类疾病方面表现优于基线模型.
结论:
- YOLO-TPS为智能鱼类疾病诊断提供了强大且可扩展的解决方案.
- 该模型显著提高了早期病变检测和诊断的准确性.
- 这项技术支持可持续的水产养殖和有效的动物健康监测.
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