基于迁移学习的水下机器人目标检测

2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) Pub Date : 2020-08-01 DOI:10.1109/ARIS50834.2020.9205774

Chia-Chin Wang, H. Samani

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引用次数: 4

摘要

本文对迁移学习方法在水下目标检测中的应用进行了体验和评价。利用YOLO的深度学习方法对水下不同类型的鱼类进行检测。采用带摄像头的ROV进行水下视频流传输，并在主计算机上对数据进行了分析。实验结果表明，采用迁移学习技术，mAP的性能提高了4%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Object Detection using Transfer Learning for Underwater Robot

In this paper the usage of Transfer Learning method for object detection in underwater environment is experienced and evaluated. Deep learning method of YOLO is utilized for detection of different types of fish underwater. A ROV equipped with camera is employed for video streaming underwater and the data has been analyzed on the main computer Our experimental results confirmed improvement in the mAP by 4% using transfer learning.

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2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS)

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