Fish Monitoring from Low-Contrast Underwater Images

Abstract

A toolset supporting fish detection, orientation, tracking and especially morphological feature estimation with high speed and accuracy, is presented in this paper. It can be exploited in fish farms to automate everyday procedures including size measurement and optimal harvest time estimation, fish health assessment, quantification of feeding needs, etc. It can also be used in an open sea environment to monitor fish size, behavior and the population of various species. An efficient deep learning technique for fish detection is employed and adapted, while methods for fish tracking are also proposed. The fish orientation is classified in order to apply a shape alignment technique that is based on the Ensemble of Regression Trees machine learning method. Shape alignment allows the estimation of fish dimensions (length, height) and the localization of fish body parts of particular interest such as the eyes and gills. The proposed method can estimate the position of 18 landmarks with an accuracy of about 95% from low-contrast underwater images where the fish can be hardly distinguished from its background. Hardware and software acceleration techniques have been applied at the shape alignment process reducing the frame processing latency to less than 0.5 us on a general purpose computer and less than 16 ms on an embedded platform. As a case study, the developed system has been trained and tested with several Mediterranean fish species in the category of seabream. A large public dataset with low-resolution underwater videos and images has also been developed to test the proposed system under worst case conditions.