一个用于在浅水中检测和细分水下海洋废物的数据集
Antun Đuraš1, Ben J Wolf2, Athina Ilioudi3
1Authors are with the Laboratory for Intelligent Autonomous Systems (LARIAT), Department of Electrical Engineering and Computing, University of Dubrovnik, Dubrovnik, Croatia. antun.djuras@unidu.hr.
Scientific data
|August 24, 2024
概括
本研究介绍了Seaclear海洋碎片数据集,这是培训强大的水下物体检测模型的宝贵资源. 该数据集有助于通过解决非结构化环境中的数据限制来实现海洋垃圾收集的自动化.
科学领域:
- 海洋生物学 海洋生物学
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 自动水下海洋垃圾收集需要强大的物体检测.
- 监督深度学习因有限的注释数据集和环境变化而难以处理水下数据.
- 现有的方法在不同的水下成像条件和位置上缺乏概括性.
研究的目的:
- 介绍Seaclear海洋碎片数据集,这是第一个公开可用的浅水数据集,例如细分和物体检测.
- 使用Faster RCNN和YOLOv6模型提供基线物体检测结果.
- 强调水下物体检测领域转移的挑战.
主要方法:
- 在多个地点使用远程操作车辆 (ROV) 收集了8610张图像.
- 标注 40 个物体类别,包括海洋垃圾,动物,植物和机器人部件.
- 用Faster RCNN和YOLOv6进行了对象检测的技术验证.
主要成果:
- 海洋海洋碎片数据集现在是公开的.
- 基线模型证明了对海洋垃圾的物体检测的可行性.
- 不同站点和摄像头之间的域名转移显著影响了模型概括.
结论:
- 西克莱尔数据集对于开发更强大的水下物体检测模型至关重要.
- 需要进一步的研究,以克服现实世界的应用领域转移的挑战.
- 这一数据集将推进自动化海洋垃圾收集技术.
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