水下声场目标识别数据

水声目标识别技术是水下信息识别和水下信息对抗的重要支撑技术，其核心是水下目标水声特征提取。水声目标识别是国家海洋经济安全、军事安全的重要战略支点，在渔业生产作业、海洋搜救、军事舰船监测、科学研究等方面发挥重要作用。 目前，基于深度学习的水下声场目标识别方法在数据源质量高、规模大时准确率高，具备较强的泛化能力，能充分拟合数据集。该方法性能优异、效果明显，但是也存在缺乏高质量数据集、特征精度不足、水下复杂声场识别率欠佳等难题。Underwater acoustic target recognition technology is an important supporting technology for underwater information recognition and underwater information confrontation, and its core lies in the acoustic feature extraction of underwater targets. Underwater acoustic target recognition is an important strategic pillar for national marine economic security and military security, and plays a vital role in fishery production operations, marine search and rescue, military ship monitoring, scientific research and other fields. Currently, underwater acoustic field target recognition methods based on deep learning achieve high accuracy when the data source has high quality and large scale, possess strong generalization ability, and can fully fit the dataset. This method has excellent performance and obvious effects, but it also faces challenges such as the lack of high-quality datasets, insufficient feature accuracy, and poor recognition rate for complex underwater acoustic fields.

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该数据集专注于水下声场目标识别，采用深度学习技术，适用于船舶细粒度分类预测，尤其在恶劣视觉条件下具有高实用价值。数据集由青岛国实科技集团有限公司申请，主要服务于科学研究和技术服务业，对海洋经济安全和军事安全具有重要战略意义。