构建首个针对寄生蜂的高分辨率图像数据集，助力自动分类。
Descriptor: Parasitoid Wasps and Associated Hymenoptera Dataset (DAPWH)
- 聚焦新热带区寄生蜂，收集3556张高清图像。
- 1739张图带多类别标注，含体部、翅脉和比例尺。
- 适合做昆虫图像识别与生物多样性监测的研究者使用。
准确的分类鉴定是生物多样性监测与农业管理的基础，尤其对于高度多样化的姬蜂总科（Ichneumonoidea）。该类寄生蜂包括姬蜂科（Ichneumonidae）和茧蜂科（Braconidae），在调控昆虫种群方面生态作用关键，但因形态隐秘且未描述物种极多，分类极为困难。为弥补此类关键类群数字资源匮乏的问题，我们构建了一个经过精心筛选的图像数据集，以推动自动化识别系统的发展。数据集包含3,556张高分辨率图像，主要覆盖新热带区的姬蜂科和茧蜂科，同时包含安德雷尼蜂科（Andrenidae）、蜜蜂科（Apidae）、贝氏蜂科（Bethylidae）、金蜂科（Chrysididae）、集蜂科（Colletidae）、熊蜂科（Halictidae）、木蜂科（Megachilidae）、泥蜂科（Pompilidae）和胡蜂科（Vespidae）等补充类群，以提升模型鲁棒性。关键的是，其中1,739张图像采用COCO格式标注，包含全虫体、翅脉及比例尺的多类别边界框。该资源为开发能够识别这些类群的计算机视觉模型奠定了基础。
原文摘要 · Abstract (English)
Accurate taxonomic identification is the cornerstone of biodiversity monitoring and agricultural management, particularly for the hyper-diverse superfamily Ichneumonoidea. Comprising the families Ichneumonidae and Braconidae, these parasitoid wasps are ecologically critical for regulating insect populations, yet they remain one of the most taxonomically challenging groups due to their cryptic morphology and vast number of undescribed species. To address the scarcity of robust digital resources for these key groups, we present a curated image dataset designed to advance automated identification systems. The dataset contains 3,556 high-resolution images, primarily focused on Neotropical Ichneumonidae and Braconidae, while also including supplementary families such as Andrenidae, Apidae, Bethylidae, Chrysididae, Colletidae, Halictidae, Megachilidae, Pompilidae, and Vespidae to improve model robustness. Crucially, a subset of 1,739 images is annotated in COCO format, featuring multi-class bounding boxes for the full insect body, wing venation, and scale bars. This resource provides a foundation for developing computer vision models capable of identifying these families.
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