Analysis of Underwater Image Processing Methods for Annotation in Deep Learning Based Fish Detection
José-Luis Lisani, Ana Belén Petro, Catalina Sbert, Amaya Álvarez-Ellacuría, Ignacio A. Catalán, Miquel Palmer
Abstract
With the advent of deep-learning (DL) techniques, image annotation has become a fundamental part of the research process. In the case of underwater image annotation, the human in charge of the task is faced whith the inherent quality problems of this kind of images. A large number of underwater image enhancement (UIE) methods have been developed aimed at improving the colors and contrast of these images. However, no attention has been paid to the specific problem of image annotation. In this case the global image quality of the processed image is less important than the fact that the objects to be annotated stand out and that their contours are easy to delineate. In this paper we evaluate seven state-of-the-art UIE techniques and rank them, through a subjective approach, according to their utility for the annotation process. The conclusion of our study is that, in general, the model-free Multiscale Retinex algorithm is preferred over more complex techniques that try to model the formation of the underwater images.