Action Learning for Coral Detection and Species Classification

Junhong Xu, Lantao Liu
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引用次数: 1

Abstract

This paper presents a method for exploring and monitoring coral reef habitats using an autonomous underwater vehicle (AUV) equipped with an onboard camera. To accomplish this task, the vehicle needs to learn to detect and classify different coral species, and also make motion decisions for exploring larger unknown areas while trying to detect as more corals (with species labels) as possible. We propose a systematic framework that integrates object detection, occupancy grid mapping, and reinforcement learning methods. To enable the vehicle to adjudicate decisions between exploration of larger space and exploitation of promising areas, we propose a reward function that combines both an information-theoretic objective for environment spatial coverage and an ingredient that encourages coral detection. We have validated the proposed method through extensive simulations, and the results show that our approach can achieve a good performance even by training with a small number of images (50 images in total) collected in the simulator.
珊瑚探测和物种分类的行动学习
本文提出了一种利用装有机载摄像机的自主水下航行器(AUV)对珊瑚礁栖息地进行探测和监测的方法。为了完成这项任务,车辆需要学习检测和分类不同的珊瑚物种,并在尝试检测尽可能多的珊瑚(带有物种标签)的同时,为探索更大的未知区域做出运动决策。我们提出了一个集成了目标检测、占用网格映射和强化学习方法的系统框架。为了使车辆能够在探索更大的空间和开发有前途的区域之间做出决定,我们提出了一个奖励函数,该函数结合了环境空间覆盖的信息论目标和鼓励珊瑚探测的成分。我们通过大量的仿真验证了所提出的方法,结果表明,即使在模拟器中收集少量图像(总共50张图像)进行训练,我们的方法也能取得良好的性能。
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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