EAPSI: A Novel Approach to Subsea Length Estimation Using Cameras in Parallel and Open-Source Software.

EAPSI：一种使用并行相机和开源软件进行海底长度估计的新方法。

• 批准号: 1713947
• 负责人: Garrett Johnson
• 金额: $ 0.54万
• 依托单位: Johnson Garrett B
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1713947&HistoricalAwards=false
• 关键词: EAPSI; Novel; Approach; Subsea; Length

Since the dawn of ecological studies of coral reefs in the 1950s, scientists have documented lengths of fish populations which indicate the overall health of fishery stocks and their habitat. The traditional method for fish length estimations is diver-based, where a researcher observes fish underwater and records length estimations by hand. Due to the optical properties of water these estimations are difficult and subject to researcher biases. Generally, fish lengths are averaged and grouped into a few size classes (Ex. 5 - 10cm, 11 - 20cm, 21 cm+). To increase the accuracy underwater length estimates, stereovideo cameras can be used, where two cameras are run in parallel and software calculates lengths of objects of interest to the nearest millimeter, a magnitude of greater accuracy than diver-based visual census techniques. The problem with using these systems over traditional methods is the high cost, which prevents widespread use.Accurate fish length records are critical for fishery resource managers in determining the overall health of coral reef systems to ensure that they remain healthy for future generations. In this study I will use OpenCV open-source software tools and low-cost GoPro underwater video cameras to develop an underwater diver based stereovideo system at a fraction of the cost of currently available systems. In Taiwan, I will be working with Dr. Colin Wen of Tunghai University, an expert on current fish length estimation techniques. Additionally, my low cost system will be used in collaboration with Dr. Wen and Dr. Allen Chen of Academia Sinica, in support of a current survey of commercially important fishery species of Taiwan. This award, under the East Asia and Pacific Summer Institutes program, supports summer research by a U.S. graduate student and is jointly funded by NSF and the Ministry of Science and Technology of Taiwan.

自 20 世纪 50 年代开始对珊瑚礁进行生态研究以来，科学家们记录了鱼类种群的长度，这表明了渔业资源及其栖息地的整体健康状况。估计鱼类长度的传统方法是基于潜水员，研究人员在水下观察鱼类并手动记录长度估计。由于水的光学特性，这些估计很困难并且容易受到研究人员的偏差。一般来说，鱼的长度会被平均并分为几个尺寸等级（例如 5 - 10 厘米、11 - 20 厘米、21 厘米+）。为了提高水下长度估计的准确性，可以使用立体视频摄像机，其中两个摄像机并行运行，软件计算感兴趣物体的长度，精确到毫米，比基于潜水员的目视普查技术的精度更高。与传统方法相比，使用这些系统的问题是成本高昂，从而阻碍了广泛使用。准确的鱼长度记录对于渔业资源管理者确定珊瑚礁系统的整体健康状况以确保它们为子孙后代保持健康至关重要。在本研究中，我将使用 OpenCV 开源软件工具和低成本 GoPro 水下摄像机来开发基于水下潜水员的立体视频系统，其成本仅为当前可用系统的一小部分。在台湾，我将与东海大学的 Colin Wen 博士合作，他是当前鱼类长度估计技术的专家。此外，我的低成本系统将与中央研究院的文博士和陈艾伦博士合作使用，以支持当前对台湾具有重要商业价值的渔业物种的调查。该奖项属于东亚和太平洋地区暑期学院计划，支持美国研究生的暑期研究，由美国国家科学基金会和台湾科学技术部共同资助。