The ocean is an important ecosystem, and aquatic animals play an important role in the
biological world, especially in aquaculture. How to accurately and intelligently recognise …

Computer vision has been applied to fish recognition for at least three decades. With the
inception of deep learning techniques in the early 2010s, the use of digital images grew …

Recently, deep neural networks (DNNs) promote mainly by network architectures and loss
functions; however, the development of neuron models has been quite limited. In this study …

Marine scientists use remote underwater image and video recording to survey fish species
in their natural habitats. This helps them get a step closer towards understanding and …

In aquaculture, Behavioral monitoring of fish contributes to scientific management and
reduces the threat of loss from disease and stress. Fish tracking technology plays an …

Today, internet and social media is used by many people, both for communication and for
expressing opinions about various topics in many domains of life. Various artificial …

The detection of underwater fish targets is critical for ecological monitoring and marine
biodiversity research. However, underwater fish detection is typically constrained by …

Aquaculture net pen defects such as biofouling, vegetation, and holes are key challenges to
efficient and sustainable fish production in aquaculture. These defects must be monitored to …

Automated monitoring and analysis of fish's growth status and behaviors can help scientific
aquaculture management and reduce severe losses due to diseases or overfeeding. With …

Machine-assisted object detection and classification of fish species from Baited Remote
Underwater Video Station (BRUVS) surveys using deep learning algorithms presents an …