How can researchers counting fish populations with deep learning?
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Researchers can count fish populations using deep learning by employing deep learning models and techniques specifically designed for this task. Deep learning networks, such as multi-modules and attention mechanism (MAN) models, have been proposed to accurately count fish in images where fish bodies are overlapped and undergo shape changes . These models consist of feature extraction modules, attention modules, and density map estimation modules, which work together to identify critical information in dense counting processes and represent the distribution and number of fishes in the image . Additionally, deep learning regression can be used to automate counting tasks without the need for individual object labeling, making it suitable for counting fish in digital imagery . By leveraging already existing image-level annotations, deep learning regression models can provide accurate counts, which are directly usable in ecological research .
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57 Citations | The researchers propose an automatic fish counting method using a hybrid neural network model that combines a multi-column convolution neural network (MCNN) and a dilated convolution neural network (DCNN). |
Open access•Posted Content | Researchers can count fish populations using deep learning by employing a density-based regression approach with a large dataset of sonar videos and utilizing self-supervised learning to improve the supervised counting task. Uncertainty quantification is also introduced to improve model training and provide a measure of prediction uncertainty for informed decision-making. |
9 Citations | The researchers propose a deep learning network model based on multi-modules and attention mechanism (MAN) to count fish populations. They use a feature extraction module, attention module, and density map estimation module to accurately identify and count the fish in images. |
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