Comparative Study of Semantic Segmentation Methods in Harbour Infrastructures

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Nunes, A (Author) Other; Gaspar, AR (Author) Other; Aníbal Castilho Coimbra de Matos (Author) FEUP

Conference proceedings International

Title: OCEANS 2023 - LIMERICK

OCEANS Conference

Limerick, IRELAND, JUN 05-08, 2023

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Z-5W1

DOI: 10.1109/oceanslimerick52467.2023.10244570

Abstract (EN): Nowadays, the semantic segmentation of the images of the underwater world is crucial, as these results can be used in various applications such as manipulation or one of the most important in the semantic mapping of the environment. In this way, the structure of the scene observed by the robot can be recovered, and at the same time, the robot can identify the class of objects seen and choose the next action during the mission. However, semantic segmentation using cameras in underwater environments is a non-trivial task, as it depends on the quality of the acquired images (which change over time due to various factors), the diversification of objects and structures that can be inspected during the mission, and the quality of the training performed prior to the evaluation, as poor training means an incorrect estimation of the object class or a poor delineation of the object. Therefore, in this paper, a comparative study of suitable modern semantic segmentation algorithms is conducted to determine whether they can be used in underwater scenarios. Nowadays, it is very important to equip the robot with the ability to inspect port facilities, as this scenario is of particular interest due to the large variety of objects and artificial structures, and to know and recognise most of them. For this purpose, the most suitable dataset available online was selected, which is the closest to the intended context. Therefore, several parameters and different conditions were considered to perform a complete evaluation, and some limitations and improvements are described. The SegNet model shows the best overall accuracy, reaching more than 80%, but some classes such as robots and plants degrade the quality of the performance (considering the mean accuracy and the mean IoU metric).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9