# Software Application for Automatic Detection and Analysis of Biomass in Underwater Videos
## 1 Introduction
These approaches include: using object features (especially size) without any classification; involving experts for manual identification of images; employing machine classification with subsequent visual inspection and validation/correction of some taxa or all images; and utilizing machine classification alone. However, in the majority of cases, marine snow is considered a singular, diverse entity due to the complexity of defining objective categories, despite its significant numerical predominance. All these strategies share, in the methodological flow to obtain abundance, the initial stages, i.e., from the way of acquiring the images through the preprocessing of the images stage to obtaining the features of each frame (especially item size) stage. From the features obtained, different classifiers can be applied automatically, supervised by experts, or through manual classification by experts. Quantitative imaging system technologies, aided by automation, offer promising tools for globally observing marine snow or plankton. These systems capture numerous images in a controlled manner, enabling the extraction of consistent features of individual objects (e.g., size, opacity) and calculating in situ concentrations from a small image volume. The instruments use various technologies, such as monochromatic lateral laser illumination, color imaging, light field, and dark field. Some systems operate in the laboratory, in situ moorings, or open waters, with low flow field perturbations ideal for imaging fragile objects. Recent developments focus on miniaturized cameras mounted on autonomous vehicles, enhancing spatiotemporal coverage compared to manned submernsibles or ships. Now that image acquisition is mature, effort should be directed towards the development of software tools based on standard terms and taxonomy to facilitate data exchange and should allow the development of quality control procedures to ensure data consistency. Moreover, to take advantage of this infrastructure and the data it contains, oceanographers must be trained in data science, not only to take advantage of machine learning methods but also simply to deal with the huge data sets involved [28]. In the Canary Islands, different studies of the DSL biomass abundance and composition, as well as migration patterns, have been developed with extractive methods [9; 29] and acoustics [30; 31]. Under the project DeepCom (DEEPCOMCTM2017-88686-P), video recordings of the DSL were obtained with low-cost technology. The Delphi system consists of a 1000-m waterproof housing made from a modified scuba tank with a front window, into which a SONY HDR-AS20 high-definition camera is inserted. The system has a lighting system with automatic on/off, consisting of two side spotlights equipped with three white LED lights with 1200 lumens of power. The system was equipped with stabilizers to maintain a fixed direction with respect to the prevailing current in the water column. The videos obtained with the Delphi system were analyzed taxonomically for big organisms [31]. No small items were considered in this analysis. The aim of this investigation was to design a software application with a client-server-based architecture with the capability to automatically quantify objects in underwater recordings, including small-sized items. Previously, the analysis of such data required continuous expert supervision. By automating the analysis, detection, and data extraction processes, the ability of researchers to handle a substantial volume of images with ease and efficiency will be enhanced, leading to significantly more precise scientific results and improving the experimental setup. The design criteria encompass the utilization of open-source software, adaptation to the distinctive attributes of underwater videos, and versatility in operation across a diverse range of databases, even when handling medium- to low-quality images. The use of standard image processing tools has been shown to be sufficient to fulfill these objectives.