PDF(Pubmed)
Abstract：
This study outlines a method for using surveillance cameras and an algorithm that calls a deep learning model to generate video segments featuring salmon and trout in small streams. This automated process greatly reduces the need for human intervention in video surveillance. Furthermore, a comprehensive guide is provided on setting up and configuring surveillance equipment, along with instructions on training a deep learning model tailored to specific requirements. Access to video data and knowledge about deep learning models makes monitoring of trout and salmon dynamic and hands-on, as the collected data can be used to train and further improve deep learning models. Hopefully, this setup will encourage fisheries managers to conduct more monitoring as the equipment is relatively cheap compared with customized solutions for fish monitoring. To make effective use of the data, natural markings of the camera-captured fish can be used for individual identification. While the automated process greatly reduces the need for human intervention in video surveillance and speeds up the initial sorting and detection of fish, the manual identification of individual fish based on natural markings still requires human effort and involvement. Individual encounter data hold many potential applications, such as capture-recapture and relative abundance models, and for evaluating fish passages in streams with hydropower by spatial recaptures, that is, the same individual identified at different locations. There is much to gain by using this technique as camera captures are the better option for the fish\'s welfare and are less time-consuming compared with physical captures and tagging.
摘要：
这项研究概述了一种使用监控摄像头的方法和一种算法，该算法调用深度学习模型来生成以小流鲑鱼和鳟鱼为特征的视频片段。这种自动化过程大大减少了视频监控中人为干预的需求。此外，提供了有关设置和配置监视设备的全面指南，以及有关培训适合特定需求的深度学习模型的说明。访问有关深度学习模型的视频数据和知识使对鳟鱼和鲑鱼的监控变得动态和动手，因为收集的数据可用于训练和进一步改进深度学习模型。希望,这种设置将鼓励渔业管理人员进行更多的监测，因为与定制的鱼类监测解决方案相比，设备相对便宜。为了有效利用数据，相机捕获的鱼的自然标记可用于个人识别。虽然自动化过程大大减少了视频监控中人为干预的需求，并加快了鱼类的初始分类和检测速度，基于自然标记的人工识别单个鱼类仍然需要人工的努力和参与。个人遭遇数据拥有许多潜在的应用，如捕获-再捕获和相对丰度模型，并通过空间捕获来评估水力发电中的鱼类通道，也就是说,在不同位置识别的同一个人。使用这种技术可以获得很多收益，因为相机捕获是鱼的福利的更好选择，并且与物理捕获和标记相比耗时更少。
