Uso de aprendizado profundo no reconhecimento de peixes do gênero Pterois em imagens subaquáticas

Abstract

This study aims to investigate the application of deep learning techniques in the recognition of Pterois species in underwater images, with the goal of assisting in the identification and monitoring of this marine species. Accurate fish recognition is essential for marine biodiversity studies and plays a key role in environmental conservation strategies. The growing use of underwater images and the advancement of artificial intelligence techniques have shown great potential for the automatic recognition of species. This research is based on the use of deep neural network architectures, such as Convolutional Neural Networks (CNNs) and Transformers, which are capable of learning and extracting relevant features from images, playing a crucial role in the identification of marine species. The study includes the use of image preprocessing techniques aimed at improving the quality of underwater images, reducing issues caused by low visibility and noise in the captures. The objective of this work is to contribute to the advancement of fish recognition methodologies through deep learning, while highlighting the importance of artificial intelligence in marine biology and the preservation of aquatic ecosystems. A comparison between different deep learning architectures will be conducted, focusing on the performance and accuracy of each approach for the recognition of Pterois species.