使用深度学习技术进行鱼类物种分类的多类框架.
Zain Farooq1, Muhammad Ramzan2, Muhammad Bilal3
1Department of Computer Science, Faculty of Computing and Information Technology, University of Sargodha, Sargodha, Pakistan.
PloS one
|February 12, 2026
概括
这项研究介绍了一种使用You Only Look Once (YOLO) 深度学习的自动化鱼类识别系统. 人工智能模型实现了99%的准确性,大大改善了渔业管理和生物多样性监测.
科学领域:
- 海洋生物学和生态学
- 人工智能在渔业中的应用
- 生物多样性的计算机视觉
背景情况:
- 准确的鱼类物种识别对于生态研究,渔业管理和生物多样性保护至关重要.
- 手动识别方法不准确且昂贵,突出了对自动化解决方案的需求.
- 深度学习,特别是卷积神经网络 (CNN),显示出希望,但面临的挑战是复杂的环境因素和有限的数据集.
研究的目的:
- 开发和评估用于自动识别鱼类物种的深度学习系统,使用YOLO物体检测范式.
- 解决手动识别和现有的自动化系统在准确分类鱼类的局限性.
- 评估拟议的YOLO架构在Fish-Pak数据集上的性能,将其与其他YOLO版本进行比较.
主要方法:
- 用了You Only Look Once (YOLO) 深度学习框架来进行对象检测.
- 训练并测试了Fish-Pak数据集上的模型,包括6种热带鱼类的915张图像.
- 在拟议的YOLO架构和YOLO v3和v4之间进行实验性比较,以评估性能.
主要成果:
- 在鱼类物种识别方面实现了99%的高整体准确性.
- 获得了99.65%的平均平均精度 (mAP),表明了卓越的检测性能.
- 与现有的关于鱼类物种识别的文献相比,证明了最高性能结果.
结论:
- 开发的深度学习系统有效地识别高精度的鱼类物种.
- 应用于Fish-Pak数据集的YOLO范式为自动化鱼类识别提供了强大而高效的解决方案.
- 这一进步对生态监测,渔业管理和海洋保护工作有重大影响.
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