A Review of Methods for Ship Detection with Electro-Optical Images in Marine Environments
Liqian Wang, Shuzhen Fan, Yunxia Liu, Yongfu Li, Cheng Fei, Junliang Liu, Bohan Liu, Yakui Dong, Zhaojun Liu, Xian Zhao
Abstract
The ocean connects all continents and is an important space for human activities. Ship detection with electro-optical images has shown great potential due to the abundant imaging spectrum and, hence, strongly supports human activities in the ocean. A suitable imaging spectrum can obtain effective images in complex marine environments, which is the premise of ship detection. This paper provides an overview of ship detection methods with electro-optical images in marine environments. Ship detection methods with sea–sky backgrounds include traditional and deep learning methods. Traditional ship detection methods comprise the following steps: preprocessing, sea–sky line (SSL) detection, region of interest (ROI) extraction, and identification. The use of deep learning is promising in ship detection; however, it requires a large amount of labeled data to build a robust model, and its targeted optimization for ship detection in marine environments is not sufficient.