Progress in the Automated Identification, Measurement, and Counting of Fish in Underwater Image Sequences

Abstract

AbstractUnderwater video systems are widely used for counting and measuring fish in aquaculture, fisheries, and conservation management. To determine population counts, spatial or temporal frequencies, and age or weight distributions, snout to tail fork length measurements are performed in video sequences, most commonly using a point and click process by a human operator. Current research aims to automate the identification, measurement, and counting of fish in order to improve the efficiency of population counts or biomass estimates. A fully automated process requires the detection and isolation of candidates for measurement, followed by the snout to tail fork length measurement, species classification, as well as the counting and tracking of fish. This paper reviews the algorithms used for the detection, identification, measurement, counting, and tracking of fish in underwater video sequences. The paper analyzes the most commonly used approaches, leading to an evaluation of the techniques most likely to be a comprehensive solution to the complete process of candidate detection, species identification, length measurement, and population counts for biomass estimation.