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[2602.20028] Descriptor: Dataset of Parasitoid Wasps and Associated Hymenoptera (DAPWH)
Summary
The article presents a curated dataset of parasitoid wasps and associated Hymenoptera, aimed at enhancing automated identification systems through high-resolution images and annotations.
Why It Matters
This dataset addresses the challenges in taxonomic identification of parasitoid wasps, crucial for biodiversity monitoring and agricultural management. By providing a robust digital resource, it supports advancements in computer vision applications in entomology.
Key Takeaways
- The dataset includes 3,556 high-resolution images of parasitoid wasps.
- 1,739 images are annotated in COCO format for enhanced model training.
- Focus on Neotropical Ichneumonidae and Braconidae, critical for insect population regulation.
- The resource aids in developing automated identification systems for challenging taxa.
- Improves model robustness by including supplementary insect families.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.20028 (cs) [Submitted on 20 Feb 2026] Title:Descriptor: Dataset of Parasitoid Wasps and Associated Hymenoptera (DAPWH) Authors:Joao Manoel Herrera Pinheiro, Gabriela Do Nascimento Herrera, Luciana Bueno Dos Reis Fernandes, Alvaro Doria Dos Santos, Ricardo V. Godoy, Eduardo A. B. Almeida, Helena Carolina Onody, Marcelo Andrade Da Costa Vieira, Angelica Maria Penteado-Dias, Marcelo Becker View a PDF of the paper titled Descriptor: Dataset of Parasitoid Wasps and Associated Hymenoptera (DAPWH), by Joao Manoel Herrera Pinheiro and 9 other authors View PDF HTML (experimental) Abstract: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, Halicti...
