低分辨率的鱼类物种识别 - - 一项使用增强超分辨率生成对抗网络 (ESRGAN),YOLO和VGG-16进行的研究
Subhrangshu Adhikary1, Saikat Banerjee2, Rajani Singh3
1Research and Development, Spiraldevs Automation Industries Pvt. Ltd., Raiganj, West Bengal, India.
PeerJ. Computer science
|June 26, 2025
概括
这项研究引入了使用深度学习的智能鱼类检测和识别模型,达到96.5%的准确性. 这项技术可以通过自动化物种分类来显著帮助渔业.
科学领域:
- 渔业 科学 渔业 科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 全球渔业需要先进的自动化解决方案来识别和分类物种.
- 目前的鱼类检测方法在准确性,可扩展性和处理多种物种或低分辨率图像方面存在局限性.
研究的目的:
- 开发一种智能模型,从摄像头录像中准确地检测,识别和定位鱼类.
- 使用生成对抗网络来提高图像分辨率以提高检测性能.
主要方法:
- 修改后的You Only Look Once (YOLO) 模型与VGG-16深度学习架构集成,用于检测和定位.
- 使用增强超级分辨率生成对抗网络 (ESRGAN) 算法,将图像分辨率提高了四倍.
主要成果:
- 综合模型在9个鱼类物种的9,460张图像数据集上实现了96.5%的整体检测准确度.
- 该ESRGAN算法成功地放大了图像分辨率,有助于提高检测性能.
结论:
- 拟议的深度学习模型为自动化鱼类物种识别和定位提供了可扩展和准确的解决方案.
- 这项技术有可能与采集和放置机器集成,以在渔业中进行高效的大规模分类.
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