水下
人工智能
计算机视觉
目标检测
计算机科学
对象(语法)
强化学习
集合(抽象数据类型)
过程(计算)
视觉对象识别的认知神经科学
人类视觉系统模型
模式识别(心理学)
图像(数学)
海洋学
操作系统
地质学
程序设计语言
作者
Hao Wang,Shixin Sun,Xiao Bai,Jian Wang,Peng Ren
出处
期刊:IEEE Journal of Oceanic Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:48 (2): 443-461
被引量:33
标识
DOI:10.1109/joe.2022.3226202
摘要
This article investigates the problem of enhancing underwater visual observations for the purpose of accurate underwater object detection. Most existing underwater visual enhancement algorithms tend to follow human vision preference but do not necessarily favor the effectiveness of an object detection algorithm. We observe that it should not be the human vision preference but the object detection algorithm that knows what underwater visual enhancement configuration is most beneficial to the detection tasks. In light of this observation, we propose a reinforcement learning paradigm of configuring visual enhancement for object detection in underwater scenes. Specifically, we use underwater image features as states and object detection score increments as rewards. We set up a collection of extensible actions that consist of multiple visual enhancement algorithms. The optimal policy is learned in the form of an action sequence, which characterizes a stepwise process of visual enhancement. Experimental results validate that the sequence of visual enhancement algorithms configured with respect to the object detection algorithm is in favor of improving the detection results.
科研通智能强力驱动
Strongly Powered by AbleSci AI