# Software Application for Automatic Detection and Analysis of Biomass in Underwater Videos
## 1 Introduction  
Approximately 71% of the Earth's surface is covered by oceans, which have an average depth of about 3800 m. Despite this, the underwater ecosystem remains largely unexplored, primarily due to the challenging logistics, high budgetary demands, and lack of appropriate technology. The use of underwater video recording has been increasingly implemented across different marine ecology studies as a substitute for more invasive techniques, with successful results [1]. However, the implementation of underwater recording poses several challenges. Firstly, image recordings can be time-consuming and costly to manually process and analyze, which has led to an emerging interest in developing automatic analysis software. Secondly, unconstrained or real underwater scenes are highly noisy, with limited visibility, non-uniform illumination, low contrast, attenuated color, and blur. Therefore, preprocessing of acquired images is necessary to reduce these disturbances, improve the quality of the images, and ensure better accuracy in the algorithms applied to them [2, 3, 4]. One of the questions addressed with the use of underwater videos is the study of biomass abundance [1]. Measurements of abundance are very sensitive to the minimum size of the organisms detected. For the detection of items in a video, there are several techniques and tools whose effectiveness depends on the context in which they are applied. Such is the case of event detection [5, 6], an approach based on the use of self-organizing neural networks and a fixed camera. An event detection program is useful, for example,in a taxonomy study, but it does not solve the problem in situations where the goal is to automatically extract data such as items, sizes, etc. This is the case for obtaining biomass abundance estimations for the Deep Scattering Layer (DSL). The DSL is a high-density biomass community located in the mesopelagic layer of the water column (between 400 and 600 m deep). This community is composed of a diversity of fish, crustaceans, cephalopods, and plankton [7; 8; 9]. This layer is involved in the nutrient cycle [10; 11], and it is thought to play a major role in the diet of different species of marine megafauna. Part of the organisms that form this layer perform a vertical synchronized movement to the epipelagic layer (between 0 and 200 m deep), thus constituting what has been defined as Diel Vertical Migration (DVM), which takes place at dusk and dawn every day [12; 13]. Macroscopic amorphous aggregates, or \"marine snow\", make up a significant part of the biomass in the water column [14]. Marine snow is composed of detritus, living organisms, and inorganic matter [15; 16]. These aggregates provide a feeding source for animals in the water column [16] and in the deeper parts of the ocean [10]. The primary mechanism through which surface-derived material reaches deep water and the ocean floor is the sinking of marine snow [17]. Gaining a deeper understanding of marine snow abundance could provide valuable insights into the dynamics of deep-water communities. Several studies have used various extractive techniques to study the abundance and composition of the DSL biomass [16; 18; 19]. These sampling methods have biases due to factors such as mesh size or the evasiveness of the animals. On the other hand, the scientific community is moving towards the implementation of less invasive methods. Other methods for studying the diversity and abundance of deep-dwelling organisms in situ include video imaging [20; 21] and acoustic sampling [22; 23]. Imagery studies have also addressed estimations of the size, abundance, and distribution of marine snow [24; 25; 26; 27]. Extractive methodologies targeting the study of marine snow also face bias involving its fragility; therefore, technology allowing the study in situ could offer a solution [15]. Various approaches have arisen to tackle the challenge of estimating the abundance of phytoplankton and/or marine snow [1].