FisHook -- An Optimized Approach to Marine Specie Classification using MobileNetV2
This incremental improvement aids fisheries and marine researchers in on-site species monitoring for ecosystem management.
The paper tackled the problem of classifying marine species by optimizing the MobileNetV2 model, achieving a 99.83% average validation accuracy through dataset creation guidelines and image augmentation.
Marine ecosystems are vital for the planet's health, but human activities such as climate change, pollution, and overfishing pose a constant threat to marine species. Accurate classification and monitoring of these species can aid in understanding their distribution, population dynamics, and the impact of human activities on them. However, classifying marine species can be challenging due to their vast diversity and the complex underwater environment. With advancements in computer performance and GPU-based computing, deep-learning algorithms can now efficiently classify marine species, making it easier to monitor and manage marine ecosystems. In this paper, we propose an optimization to the MobileNetV2 model to achieve a 99.83% average validation accuracy by highlighting specific guidelines for creating a dataset and augmenting marine species images. This transfer learning algorithm can be deployed successfully on a mobile application for on-site classification at fisheries.