Abstract
The intelligent detection of offshore maritime targets using computer vision technology can provide a scientific basis for marine administrative management, marine environmental supervision and management as well as the formulation of marine environmental protection policies, providing a powerful environmental information reference for the steady development of the economy. The dataset includes the data collected from Sanduao Harbor in the southeast of Ningde City, Fujian Province, China, with Google Earth serving as the primary data source and a time span from 2019 to 2021. This dataset comprises 1,761 visible light remote sensing images acquired under different seasons, backgrounds and illumination conditions, and corresponding horizontal object detection labels, rotational object detection labels and semantic segmentation labels, covering three types of offshore maritime targets, namely ships, fish row cage culture areas, and raft culture areas. After screening and correction, it can meet the current mainstream deep learning model training needs. This dataset can provide basic data for the semantic segmentation, horizontal object detection, rotational object detection and other research fields of offshore maritime target images.
Published Version
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