An intelligent and cost-effective remote underwater video device for fish size monitoring

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. •The Underwater Detector of Moving Object Size (UDMOS) computer vision system is presented, which records large fishes passing in front of a camera•UDMOS can be deployed as un unbaited underwater system and can seamlessly work in shallow or deep waters, and with infrared or visible light•A free-to-use standardized Web Service is also proposed to run UDMOS for batch video-processing•Three alternative large-object detectors are embedded, based on deep learning, unsupervised modelling, and motion detection respectively•UDMOS is cost-effective because it uses minimalistic hardware and power consumption and spares time to experts for analysing long BRUV videos