Deep-Sea Organisms Tracking Using Dehazing and Deep Learning
Deep-Sea Organisms Tracking Using Dehazing and Deep Learning
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DOI：
10.1007/s11036-018-1117-9
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发表时间：
2018-10
影响因子：
3.8
通讯作者：
Huimin Lu-;Tomoki Uemura;Dong Wang;Jihua Zhu;Zi Huang;Hyoungseop Kim
中科院分区：
文献类型：
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作者：
Huimin Lu-;Tomoki Uemura;Dong Wang;Jihua Zhu;Zi Huang;Hyoungseop Kim
由于缺乏训练数据，深海生物自动跟踪的研究很少。然而，对于水下机器人来说，识别和预测生物的行为是极其重要的。本文首先提出了一种水下多目标实时识别与跟踪的方法，我们称之为“你只看一次：YOLO”。这种方法为我们提供了一个非常快速和准确的跟踪器。首先，我们从捕获的图像中去除由水的浑浊引起的雾霾。之后，我们应用YOLO来识别和跟踪海洋生物，包括虾、鱿鱼、螃蟹和鲨鱼。实验表明，所开发的系统具有较好的性能。
Deep-sea organism automatic tracking has rarely been studied because of a lack of training data. However, it is extremely important for underwater robots to recognize and to predict the behavior of organisms. In this paper, we first develop a method for underwater real-time recognition and tracking of multi-objects, which we call “You Only Look Once: YOLO”. This method provides us with a very fast and accurate tracker. At first, we remove the haze, which is caused by the turbidity of the water from a captured image. After that, we apply YOLO to allow recognition and tracking of marine organisms, which include shrimp, squid, crab and shark. The experiments demonstrate that our developed system shows satisfactory performance.