Improving Semantic Segmentation Performance in Underwater Images

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Nunes, A (Autor) Outra; Aníbal Castilho Coimbra de Matos (Autor) FEUP

Revista

Título: Journal of Marine Science and Engineering

Vol. 8

Página Final: 2268

Editora: MDPI

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Z-8VN

DOI: 10.3390/jmse11122268

Abstract (EN): Nowadays, semantic segmentation is used increasingly often in exploration by underwater robots. For example, it is used in autonomous navigation so that the robot can recognise the elements of its environment during the mission to avoid collisions. Other applications include the search for archaeological artefacts, the inspection of underwater structures or in species monitoring. Therefore, it is necessary to improve the performance in these tasks as much as possible. To this end, we compare some methods for image quality improvement and data augmentation and test whether higher performance metrics can be achieved with both strategies. The experiments are performed with the SegNet implementation and the SUIM dataset with eight common underwater classes to compare the obtained results with the already known ones. The results obtained with both strategies show that they are beneficial and lead to better performance results by achieving a mean IoU of 56% and an increased overall accuracy of 81.8%. The result for the individual classes shows that there are five classes with an IoU value close to 60% and only one class with an IoU value less than 30%, which is a more reliable result and is easier to use in real contexts.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 26