Monitoring the size of key indicator species of fish is important to understand ecosystem functions, anthropogenic stress, and population dynamics. Standard methodologies gather data using underwater cameras, but are biased due to the use of baits, limited deployment time, and short field of view. Furthermore, they require experts to analyse long videos to search for species of interest, which is time consuming and expensive. This paper describes the Underwater Detector of Moving Object Size (UDMOS), a cost-effective computer vision system that records events of large fishes passing in front of a camera, using minimalistic hardware and power consumption. UDMOS can be deployed underwater, as an unbaited system, and is also offered as a free-to-use Web Service for batch video-processing. It embeds three different alternative large-object detection algorithms based on deep learning, unsupervised modelling, and motion detection, and can work both in shallow and deep waters with infrared or visible light.

监测鱼类关键指标物种的大小对于了解生态系统功能，人为压力和种群动态非常重要。标准方法使用水下摄像机收集数据，但是由于使用诱饵，部署时间有限和视野短而存在偏差。此外，他们要求专家分析长视频以搜索感兴趣的物种，这既耗时又昂贵。本文介绍了一种运动物体尺寸水下检测器（UDMOS），这是一种经济高效的计算机视觉系统，它使用简约的硬件和功耗来记录在照相机前通过的大型鱼类的事件。UDMOS可以作为无诱饵系统部署在水下，也可以作为批处理视频处理的免费Web服务提供。