ShipMonitoring-LSS Dataset
ShipMonitoring-LSS dataset
Overview:
This dataset is fromthe paper titled "Addressing Unfamiliar Ship Type Recognition in Real-Scenario Vessel Monitoring: A Multi-Angle Metric Networks Framework," which explores innovative approaches to ship type recognition under real-world conditions.
The ShipMonitoring-LSS dataset is meticulously designed to address the challenges highlighted in the aforementioned paper. It serves as a cornerstone for advancing the field by providing a comprehensive collection of images and metadata focused on ship detection and classification. With a broad spectrum of ship types, categorized into both coarse (9categories) and fine (73categories), this dataset is tailored to enhance the accuracy and robustness of ship type recognition systems, as advocated in the paper.
Key Features:
Enhanced Granularity:The dataset significantly increases the granularity of ship type classification, offering a more detailed view of ship types than standard datasets, which is crucial for the research outlined in the paper.
Real-World Alignment: The ShipMonitoring-LSS dataset is crafted to closely mirror actual marine environments, directly addressing the real-world ship type recognition challenges discussed in the paper.
Few-Shot Learning Focus:With dedicated subsets for 5-shot and 10-shot learning, the dataset is perfectly suited for the few-shot learning scenarios proposed in the paper.
Contents:
Query Sets: These include 5-shot and 73-shot query sets for classification tasks, as recommended in the paper.
Support Sets:Both 5-shot and 73-shot support sets are provided to complement the few-shot learning approach.
Training Data: The dataset includes training data for 5-shot and 10-shot learning scenarios, in line with the paper's framework.
Usage:
The ShipMonitoring-LSS dataset is intended for use by researchers to:
(1) Implement and evaluate the multi-angle metric networks framework proposed in the paper.
(2) Investigate the effectiveness of few-shot learning in ship type recognition.
(3) Develop and refine ship monitoring systems based on the principles outlined in the paper.