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Fish assessment and monitoring are important for the development of a modern aquatic ecosystem. Fish are a vital part of the marine and freshwater environments. Morphological and computational details of fish, such as size, shape, and position, are important in fish observation and fisheries. Typically, manual, or low-efficient techniques are used to acquire fish details. However, existing typical methods are usually time-consuming, less accurate, and resource-intensive. Computer-aided methods are crucial for intelligent and automatic fish assessment. Two novel networks, namely parallel feature fusion-based segmentation network (PFFS-Net) and progressive information fusion-based segmentation network (PIFS-Net), were developed for pixel-wise fish segmentation. PFFS-Net is a base network that uses parallel feature fusion to achieve a better segmentation performance. PIFS-Net is the final model of this work and uses a progressive spatial feature fusion (SFF) mechanism to enhance segmentation accuracy. PIFS-Net also employs rapid feature reduction and pre-prediction low-level information fusion blocks to further boost performance. The proposed models were evaluated using the following three publicly available databases: semantic segmentation of underwater imagery (SUIM), DeepFish, and Large-scale fish. The proposed networks outperformed the state-of-the-art methods in challenging underwater conditions with superior computational efficiency. PIFS-Net needs only 2.02 million trainable parameters for its complete training. Automatic and accurate fish segmentation can be a major step towards an intelligent aquatic ecosystem. The codes of our algorithms and trained models are available on Github. [ABSTRACT FROM AUTHOR]

In this work, acoustic and computer vision techniques are combined to develop an automatic procedure for biomass estimation of tuna during transfers. A side scan sonar working at 200 kHz and a stereo camera, positioned facing towards the surface to record the ventral aspect of fish, are set as acquisition equipment. Moreover, a floating structure has been devised to place the sensors between cages in transfers, creating a transfer canal that allows data acquisition while fish swim from donor to receiving cage. Biomass assessment is computed by counting transferred tuna and sizing a representative sample of the stock. The number of transferred tuna is automatically deduced from acoustic echograms by means of image processing techniques, whereas tuna size is computed from the stereo videos using our automatic computer vision procedure based on a deformable model of the fish ventral silhouette. The results show that the system achieves automatic tuna counting with error below 10%, achieving around 1% error in the best configuration, and automatic tuna sizing of more than 20% of the stock, with highly accurate Snout Fork Length estimation when compared to true data from harvests. These results fulfil the requirements imposed by International Commission for the Conservation of Atlantic Tunas for compliant transfer operations. [ABSTRACT FROM AUTHOR]

Underwater object detection and recognition using computer vision are challenging tasks due to the poor light condition of submerged environments. For intervention missions requiring grasping and manipulation of submerged objects, a vision system must provide an Autonomous Underwater Vehicles (AUV) with object detection, localization and tracking capabilities. In this paper, we describe the integration of a vision system in the MARIS intervention AUV and its configuration for detecting cylindrical pipes, a typical artifact of interest in underwater operations. Pipe edges are tracked using an alpha-beta filter to achieve robustness and return a reliable pose estimation even in case of partial pipe visibility. Experiments in an outdoor water pool in different light conditions show that the adopted algorithmic approach allows detection of target pipes and provides a sufficiently accurate estimation of their pose even when they become partially visible, thereby supporting the AUV in several successful pipe grasping operations. [ABSTRACT FROM AUTHOR]

• Estimation of Bluefin Tuna mean length in sea cages has been performed using underwater acoustic tools. • The combined use of optical and acoustical techniques has revealed a strong relationship between TS and ABFT length measured in fattening cages. • Good agreement was found between TS and the fish fork (without the need to know the tuna's tilt angle), even considering uncompensated TS variable. This paper proposes an indirect method to estimate Bluefin Tuna (Thunnus thynnus) biomass in cages using acoustic techniques. Two Simrad EK60 echosounders working at 120 and 200 kHz and a stereo camera were used to obtain target strength (TS) to fork length (FL) relationships for both operating frequencies. The equipment was placed at the bottom of a floating cage, facing towards the surface to record the ventral aspect of fish. The acoustic and optical recordings were automatically analysed and the combination of acoustic and optical results provided unequivocal TS-FL assignments. Good relationships between TS and FL were obtained for both frequencies even without discriminating data from different fish tilts and without using beam directivity compensation. Stronger correlations were obtained for compensated TS at 200 kHz when reduced tuna swimming tilt was considered. TS measurements were compared to MFS numerical predictions for a Bluefin swimbladder model, with the simulation results showing good agreement with experimental measurements. The results allow the mean tuna length to be predicted in growing or fattening cages from acoustic data raising the possibility of improving production management and of providing a useful tool for catch control estimations made by international organisations (like ICCAT) and government bodies. [ABSTRACT FROM AUTHOR]