Deep learning networks have become increasingly popular in recent years due to promising
breakthroughs achieved in several areas. The importance of deep learning lies in the …

Unlike land, the oceans, although covering more than 70% of the planet, are largely
unexplored. Global fisheries resources are central to the sustainability and quality of life on …

Fish classification (FC) is crucial in various domains, including fishery management and
ecological research. Traditional FC methods rely mainly on morphological criteria such as …

Camera-based electronic monitoring (EM) systems are increasingly being deployed
onboard commercial fishing vessels to collect essential data for fisheries management and …

Deep convolutional neural networks generally perform well in underwater object recognition
tasks on both optical and sonar images. Many such methods require hundreds, if not …

Increasing consideration of welfare in aquaculture has prompted interest in non‐invasive
methods of monitoring that avoid unnecessary stress and handling. Machine vision (MV) …

Reconstructed images from deep learning-based compressed images are not suitable for
underwater image analysis. Hence, this paper proposes a compression framework by …

The complex underwater environment usually leads to the problem of quality degradation in
underwater images, and the distortion of sharpness and color are the main factors to the …

This paper presents a labeling methodology for marine life data using a weakly supervised
learning framework. The methodology iteratively trains a deep learning model using non …

Fish species recognition is an integral part of sustainable marine biodiversity and
aquaculture. The rapid emergence of deep learning methods has shown great potential on …