arXiv:2603.10724cs.CV2026-03

构建首个用于鲨鱼鳐类精细识别的公开图像数据集,助力海洋生物多样性保护。

eLasmobranc Dataset: An Image Dataset for Elasmobranch Species Recognition and Biodiversity Monitoring

  • 基于标准化采集,获取7种鲨鱼鳐类清晰形态图像
  • 包含专家标注与时空元数据,支持物种级分类研究
  • 适合从事海洋保护、计算机视觉的科研人员使用

鲨鱼和鳐鱼种群正面临全球性衰退,多个物种已被列为受威胁物种。可靠的监测与物种识别对支持重要鲨鱼与鳐鱼区域(ISRAs)等保护规划至关重要。然而,现有视觉数据集多为检测导向、水下获取或仅限粗粒度分类,难以满足细粒度形态识别需求。本文提出eLasmobranc数据集,包含来自西班牙东部地中海海岸七种生态相关鲨鱼鳐类的图像,该地区已划定两个ISRAs。图像通过专项调研、与当地渔市及项目合作,以及开放获取资源收集,多数在非水下环境中按标准协议拍摄,确保诊断性形态特征清晰可见。数据集整合专家验证的物种标注、结构化时空元数据及补充物种信息。专为监督式物种识别、种群研究及人工智能辅助生物多样性监测设计。通过形态清晰度、分类可靠性与公开可访问性,填补了细粒度鲨鱼鳐类识别的关键空白,推动保护导向计算机视觉的可复现研究。数据集公开获取于https://zenodo.org/records/18549737。

原文摘要 · Abstract (English)

Elasmobranch populations are experiencing significant global declines, and several species are currently classified as threatened. Reliable monitoring and species-level identification are essential to support conservation and spatial planning initiatives such as Important Shark and Ray Areas (ISRAs). However, existing visual datasets are predominantly detection-oriented, underwater-acquired, or limited to coarse-grained categories, restricting their applicability to fine-grained morphological classification. We present the eLasmobranc Dataset, a curated and publicly available image collection from seven ecologically relevant elasmobranch species inhabiting the eastern Spanish Mediterranean coast, a region where two ISRAs have been identified. Images were obtained through dedicated data collection, including field campaigns and collaborations with local fish markets and projects, as well as from open-access public sources. The dataset was constructed predominantly from images acquired outside the aquatic environment under standardized protocols to ensure clear visualization of diagnostic morphological traits. It integrates expert-validated species annotations, structured spatial and temporal metadata, and complementary species-level information. The eLasmobranc Dataset is specifically designed to support supervised species-level classification, population studies, and the development of artificial intelligence systems for biodiversity monitoring. By combining morphological clarity, taxonomic reliability, and public accessibility, the dataset addresses a critical gap in fine-grained elasmobranch identification and promotes reproducible research in conservation-oriented computer vision. The dataset is publicly available at https://zenodo.org/records/18549737.

生物识别海洋保护图像数据集AI监测

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