Multiple Vessel Detection in Harsh Maritime Environments

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Duarte, DF (Autor) Outra; Pereira, MI (Autor) Outra; Pinto, AM (Autor) FEUP

Revista

Título: Marine Technology Society Journal

Vol. 56

Páginas: 58-67

ISSN: 0025-3324

Editora: Marine Technology Society, Inc.

Indexação

ISI Web of Knowledge - 0 Citações

Outras Informações

ID Authenticus: P-00X-HNC

Abstract (EN): Recently, research concerning the navigation of autonomous surface vehicles (ASVs) has been increasing. However, a large-scale implementation of these vessels is still held back by several challenges such as multi-object tracking. Attaining accurate object detection plays a big role in achieving successful tracking. This article presents the development of a detection model with an image-based Con-volutional Neural Network trained through transfer learning, a deep learning tech-nique. To train, test, and validate the detector module, data were collected with the SENSE ASV by sailing through two nearby ports, Leixoes and Viana do Castelo, and recording video frames through its on-board cameras, along with a Light De-tection And Ranging, GPS, and Inertial Measurement Unit data. Images were ex-tracted from the collected data, composing a manually annotated dataset with nine classes of different vessels, along with data from other open-source maritime datasets. The developed model achieved a class mAP@[.5 .95] (mean average precision) of 89.5% and a clear improvement in boat detection compared to a multi-purposed state-of-the-art detector, YOLO-v4, with a 22.9% and 44.3% increase in the mAP with an Intersection over Union threshold of 50% and the mAP@[.5 .95], respectively. It was integrated in a detection and tracking system, being able to continuously detect nearby vessels and provide sufficient informa-tion for simple navigation tasks.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10