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Lin Du

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Publications (1)

Proceedings Article | 30 April 2024 Paper

YJWS2023: high-resolution optical remote sensing image dataset of typical military ship target

Lan Ma, Xiao Wang, Lin Du

Proceedings Volume 13156, 131560L (2024) https://doi.org/10.1117/12.3015812

KEYWORDS: Remote sensing, Data modeling, Target recognition, Education and training, Target detection, Object detection, Detection and tracking algorithms, Deep learning, Automatic target recognition, Submerged target modeling

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At present, deep learning and other artificial intelligence technologies are constantly applied in the field of remote sensing, and deep learning needs massive data for model driving. However, the development of ship datasets based on optical remote sensing images is slow and not very professional. To solve this problem, we propose three stages of ship intelligence interpretation, and construct a new ship data set for the development of the first stage. The data set reclassifies the existing ships, combined with the purpose of ship target identification and the current development direction of military ships, and is divided into six types of labels, including aircraft carriers, escort ships, submarines, dock landing ships, supply ships and other auxiliary ships. The 909 samples were manually annotated, with the training set contains 795 samples and the validation set contains 114 samples. Compared with the commonly used optical remote sensing ship data sets in China, the data set we proposed has following properties: 1. Professional. The data set targets military ships, The data sources from all ships in the world's military ports; 2. Systemic. Three stages of automatic identification are proposed; 3. Classification and rationality. The classification of this data set combines the general ship classification criterias, practical identification needs and the developments of the ship itself. The world's military ships are divided into six categories; 4. Uneven nature. The number of sliced samples of each type of ships is unbalanced; 5. Developability. This data set can consistently increase the number of samples. New classes of ships will emerge in the short terms. The class can also be divided into six categories according to their missions; 6. The openness. All data for this data set were obtained from Internet public data. Through the verification and evaluation of this data set using five target detection algorithms of YOLO series, the accuracy of the target detection model is about 0.67 and the overall accuracy is about 0.76, which verifies the feasibility of this data set for the verification of the ship target identification algorithms.

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